Overview

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Dataset statistics

Number of variables264
Number of observations988402
Missing cells194274596
Missing cells (%)74.5%
Total size in memory1.9 GiB
Average record size in memory2.1 KiB

Variable types

Numeric35
Unsupported116
Text106
Boolean7

Dataset

DescriptionUS NMNH Extant Specimen Records 0052487-241126133413365
URLhttps://doi.org/10.15468/dl.wttrju

Alerts

license has constant value "CC0_1_0" Constant
publisher has constant value "National Museum of Natural History, Smithsonian Institution" Constant
institutionID has constant value "urn:lsid:biocol.org:col:15463" Constant
collectionID has constant value "urn:uuid:60e28f81-e634-4869-aa3e-732caed713c8" Constant
institutionCode has constant value "US" Constant
collectionCode has constant value "US" Constant
datasetName has constant value "NMNH Extant Biology" Constant
occurrenceStatus has constant value "PRESENT" Constant
verbatimSRS has constant value "1938-11-11" Constant
footprintSRS has constant value "315.0" Constant
footprintSpatialFit has constant value "315.0" Constant
georeferencedBy has constant value "1938.0" Constant
georeferencedDate has constant value "11.0" Constant
georeferenceSources has constant value "11 Nov 1938" Constant
latestEpochOrHighestSeries has constant value "South America - Neotropics, Colombia, Meta" Constant
earliestAgeOrLowestStage has constant value "SOUTH_AMERICA" Constant
lowestBiostratigraphicZone has constant value "7296210.0" Constant
lithostratigraphicTerms has constant value "CO" Constant
group has constant value "Meta" Constant
dateIdentified has constant value "Plantae, Dicotyledonae, Malpighiales, Violaceae, Violoideae" Constant
identificationReferences has constant value "Plantae" Constant
identificationVerificationStatus has constant value "Tracheophyta" Constant
identificationRemarks has constant value "Magnoliopsida" Constant
taxonID has constant value "Malpighiales" Constant
namePublishedInID has constant value "Rinorea" Constant
taxonConceptID has constant value "Rinorea" Constant
parentNameUsage has constant value "pubiflora" Constant
originalNameUsage has constant value "pubiflora" Constant
namePublishedIn has constant value "VARIETY" Constant
superfamily has constant value "821cc27a-e3bb-4bc5-ac34-89ada245069d" Constant
subfamily has constant value "2024-12-02T13:57:09.776Z" Constant
tribe has constant value "450.0" Constant
subtribe has constant value "50.0" Constant
infragenericEpithet has constant value "OCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_COUNT" Constant
cultivarEpithet has constant value "False" Constant
verbatimTaxonRank has constant value "7296210.0" Constant
nomenclaturalCode has constant value "7707728.0" Constant
nomenclaturalStatus has constant value "1414.0" Constant
taxonRemarks has constant value "6631.0" Constant
publishingCountry has constant value "US" Constant
subgenusKey has constant value "Magnoliopsida" Constant
protocol has constant value "EML" Constant
projectId has constant value "edulis" Constant
Unnamed: 223 has constant value "500.0" Constant
Unnamed: 224 has constant value "0.0" Constant
Unnamed: 228 has constant value "OCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_COUNT" Constant
Unnamed: 229 has constant value "StillImage" Constant
Unnamed: 230 has constant value "False" Constant
Unnamed: 231 has constant value "False" Constant
Unnamed: 232 has constant value "7634583.0" Constant
Unnamed: 233 has constant value "5541644.0" Constant
Unnamed: 234 has constant value "6.0" Constant
Unnamed: 235 has constant value "7707728.0" Constant
Unnamed: 236 has constant value "220.0" Constant
Unnamed: 237 has constant value "718.0" Constant
Unnamed: 238 has constant value "9291.0" Constant
Unnamed: 239 has constant value "3155252.0" Constant
Unnamed: 241 has constant value "5541644.0" Constant
Unnamed: 242 has constant value "Annona edulis" Constant
Unnamed: 243 has constant value "Annona edulis (Triana & Planch.) H.Rainer" Constant
Unnamed: 244 has constant value "Rollinia edulis var. acuta" Constant
Unnamed: 246 has constant value "EML" Constant
Unnamed: 247 has constant value "2024-12-02T13:56:28.527Z" Constant
Unnamed: 248 has constant value "2024-12-02T11:48:23.416Z" Constant
Unnamed: 249 has constant value "True" Constant
Unnamed: 252 has constant value "False" Constant
Unnamed: 253 has constant value "LATIN_AMERICA" Constant
Unnamed: 254 has constant value "NORTH_AMERICA" Constant
hasGeospatialIssues is highly imbalanced (95.5%) Imbalance
accessRights has 988402 (100.0%) missing values Missing
bibliographicCitation has 988402 (100.0%) missing values Missing
language has 988402 (100.0%) missing values Missing
references has 988402 (100.0%) missing values Missing
rightsHolder has 988402 (100.0%) missing values Missing
type has 988402 (100.0%) missing values Missing
datasetID has 988402 (100.0%) missing values Missing
ownerInstitutionCode has 988402 (100.0%) missing values Missing
informationWithheld has 988402 (100.0%) missing values Missing
dataGeneralizations has 988402 (100.0%) missing values Missing
dynamicProperties has 988402 (100.0%) missing values Missing
catalogNumber has 132504 (13.4%) missing values Missing
recordedBy has 11879 (1.2%) missing values Missing
recordedByID has 988402 (100.0%) missing values Missing
organismQuantity has 988402 (100.0%) missing values Missing
organismQuantityType has 988402 (100.0%) missing values Missing
sex has 988402 (100.0%) missing values Missing
lifeStage has 916836 (92.8%) missing values Missing
reproductiveCondition has 988402 (100.0%) missing values Missing
caste has 988402 (100.0%) missing values Missing
behavior has 988402 (100.0%) missing values Missing
vitality has 988402 (100.0%) missing values Missing
establishmentMeans has 988402 (100.0%) missing values Missing
degreeOfEstablishment has 988402 (100.0%) missing values Missing
pathway has 988402 (100.0%) missing values Missing
georeferenceVerificationStatus has 988402 (100.0%) missing values Missing
preparations has 959242 (97.0%) missing values Missing
disposition has 988402 (100.0%) missing values Missing
associatedOccurrences has 988402 (100.0%) missing values Missing
associatedReferences has 988402 (100.0%) missing values Missing
associatedSequences has 988328 (> 99.9%) missing values Missing
associatedTaxa has 988402 (100.0%) missing values Missing
otherCatalogNumbers has 988402 (100.0%) missing values Missing
occurrenceRemarks has 968411 (98.0%) missing values Missing
organismID has 988402 (100.0%) missing values Missing
organismName has 988402 (100.0%) missing values Missing
organismScope has 988402 (100.0%) missing values Missing
associatedOrganisms has 988402 (100.0%) missing values Missing
previousIdentifications has 988402 (100.0%) missing values Missing
organismRemarks has 988402 (100.0%) missing values Missing
materialEntityID has 988402 (100.0%) missing values Missing
materialEntityRemarks has 988402 (100.0%) missing values Missing
verbatimLabel has 988402 (100.0%) missing values Missing
materialSampleID has 988402 (100.0%) missing values Missing
eventID has 988402 (100.0%) missing values Missing
parentEventID has 988402 (100.0%) missing values Missing
eventType has 988402 (100.0%) missing values Missing
fieldNumber has 988343 (> 99.9%) missing values Missing
eventDate has 119809 (12.1%) missing values Missing
eventTime has 988402 (100.0%) missing values Missing
startDayOfYear has 261666 (26.5%) missing values Missing
endDayOfYear has 261666 (26.5%) missing values Missing
year has 122319 (12.4%) missing values Missing
month has 181983 (18.4%) missing values Missing
day has 314697 (31.8%) missing values Missing
verbatimEventDate has 655426 (66.3%) missing values Missing
habitat has 877971 (88.8%) missing values Missing
samplingProtocol has 988402 (100.0%) missing values Missing
sampleSizeValue has 988402 (100.0%) missing values Missing
sampleSizeUnit has 988402 (100.0%) missing values Missing
samplingEffort has 988402 (100.0%) missing values Missing
fieldNotes has 988402 (100.0%) missing values Missing
eventRemarks has 988402 (100.0%) missing values Missing
locationID has 979422 (99.1%) missing values Missing
higherGeographyID has 988402 (100.0%) missing values Missing
continent has 32788 (3.3%) missing values Missing
waterBody has 984227 (99.6%) missing values Missing
islandGroup has 963568 (97.5%) missing values Missing
island has 906001 (91.7%) missing values Missing
countryCode has 10855 (1.1%) missing values Missing
stateProvince has 219376 (22.2%) missing values Missing
county has 826754 (83.6%) missing values Missing
municipality has 988402 (100.0%) missing values Missing
locality has 72708 (7.4%) missing values Missing
verbatimLocality has 988402 (100.0%) missing values Missing
verbatimElevation has 988402 (100.0%) missing values Missing
verticalDatum has 988402 (100.0%) missing values Missing
verbatimDepth has 983702 (99.5%) missing values Missing
minimumDistanceAboveSurfaceInMeters has 988402 (100.0%) missing values Missing
maximumDistanceAboveSurfaceInMeters has 988402 (100.0%) missing values Missing
locationAccordingTo has 988402 (100.0%) missing values Missing
locationRemarks has 988402 (100.0%) missing values Missing
decimalLatitude has 841005 (85.1%) missing values Missing
decimalLongitude has 841005 (85.1%) missing values Missing
coordinateUncertaintyInMeters has 987002 (99.9%) missing values Missing
coordinatePrecision has 988402 (100.0%) missing values Missing
pointRadiusSpatialFit has 988402 (100.0%) missing values Missing
verbatimCoordinateSystem has 980404 (99.2%) missing values Missing
verbatimSRS has 988401 (> 99.9%) missing values Missing
footprintWKT has 988402 (100.0%) missing values Missing
footprintSRS has 988401 (> 99.9%) missing values Missing
footprintSpatialFit has 988401 (> 99.9%) missing values Missing
georeferencedBy has 988401 (> 99.9%) missing values Missing
georeferencedDate has 988401 (> 99.9%) missing values Missing
georeferenceProtocol has 960543 (97.2%) missing values Missing
georeferenceSources has 988401 (> 99.9%) missing values Missing
georeferenceRemarks has 988289 (> 99.9%) missing values Missing
geologicalContextID has 988402 (100.0%) missing values Missing
earliestEonOrLowestEonothem has 988402 (100.0%) missing values Missing
latestEonOrHighestEonothem has 988402 (100.0%) missing values Missing
earliestEraOrLowestErathem has 988402 (100.0%) missing values Missing
latestEraOrHighestErathem has 988402 (100.0%) missing values Missing
earliestPeriodOrLowestSystem has 988402 (100.0%) missing values Missing
latestPeriodOrHighestSystem has 988402 (100.0%) missing values Missing
earliestEpochOrLowestSeries has 988402 (100.0%) missing values Missing
latestEpochOrHighestSeries has 988401 (> 99.9%) missing values Missing
earliestAgeOrLowestStage has 988401 (> 99.9%) missing values Missing
latestAgeOrHighestStage has 988402 (100.0%) missing values Missing
lowestBiostratigraphicZone has 988401 (> 99.9%) missing values Missing
highestBiostratigraphicZone has 988402 (100.0%) missing values Missing
lithostratigraphicTerms has 988401 (> 99.9%) missing values Missing
group has 988401 (> 99.9%) missing values Missing
formation has 988402 (100.0%) missing values Missing
member has 988402 (100.0%) missing values Missing
bed has 988400 (> 99.9%) missing values Missing
identificationID has 988402 (100.0%) missing values Missing
verbatimIdentification has 988402 (100.0%) missing values Missing
identificationQualifier has 985985 (99.8%) missing values Missing
typeStatus has 967033 (97.8%) missing values Missing
identifiedBy has 866335 (87.7%) missing values Missing
identifiedByID has 988402 (100.0%) missing values Missing
dateIdentified has 988401 (> 99.9%) missing values Missing
identificationReferences has 988401 (> 99.9%) missing values Missing
identificationVerificationStatus has 988401 (> 99.9%) missing values Missing
identificationRemarks has 988401 (> 99.9%) missing values Missing
taxonID has 988401 (> 99.9%) missing values Missing
scientificNameID has 988402 (100.0%) missing values Missing
parentNameUsageID has 988402 (100.0%) missing values Missing
originalNameUsageID has 988402 (100.0%) missing values Missing
nameAccordingToID has 988402 (100.0%) missing values Missing
namePublishedInID has 988401 (> 99.9%) missing values Missing
taxonConceptID has 988401 (> 99.9%) missing values Missing
acceptedNameUsage has 988402 (100.0%) missing values Missing
parentNameUsage has 988401 (> 99.9%) missing values Missing
originalNameUsage has 988401 (> 99.9%) missing values Missing
nameAccordingTo has 988402 (100.0%) missing values Missing
namePublishedIn has 988401 (> 99.9%) missing values Missing
namePublishedInYear has 988402 (100.0%) missing values Missing
order has 10135 (1.0%) missing values Missing
superfamily has 988401 (> 99.9%) missing values Missing
family has 10432 (1.1%) missing values Missing
subfamily has 988401 (> 99.9%) missing values Missing
tribe has 988401 (> 99.9%) missing values Missing
subtribe has 988401 (> 99.9%) missing values Missing
genus has 15345 (1.6%) missing values Missing
genericName has 15400 (1.6%) missing values Missing
subgenus has 988402 (100.0%) missing values Missing
infragenericEpithet has 988401 (> 99.9%) missing values Missing
specificEpithet has 75483 (7.6%) missing values Missing
infraspecificEpithet has 923675 (93.5%) missing values Missing
cultivarEpithet has 988401 (> 99.9%) missing values Missing
verbatimTaxonRank has 988401 (> 99.9%) missing values Missing
vernacularName has 988400 (> 99.9%) missing values Missing
nomenclaturalCode has 988401 (> 99.9%) missing values Missing
nomenclaturalStatus has 988401 (> 99.9%) missing values Missing
taxonRemarks has 988401 (> 99.9%) missing values Missing
elevation has 625728 (63.3%) missing values Missing
elevationAccuracy has 880635 (89.1%) missing values Missing
depth has 979722 (99.1%) missing values Missing
depthAccuracy has 980482 (99.2%) missing values Missing
distanceFromCentroidInMeters has 987807 (99.9%) missing values Missing
mediaType has 69371 (7.0%) missing values Missing
orderKey has 10134 (1.0%) missing values Missing
familyKey has 10432 (1.1%) missing values Missing
genusKey has 15344 (1.6%) missing values Missing
subgenusKey has 988401 (> 99.9%) missing values Missing
speciesKey has 75442 (7.6%) missing values Missing
species has 75443 (7.6%) missing values Missing
typifiedName has 988402 (100.0%) missing values Missing
relativeOrganismQuantity has 988402 (100.0%) missing values Missing
projectId has 988401 (> 99.9%) missing values Missing
gbifRegion has 19586 (2.0%) missing values Missing
level0Gid has 854767 (86.5%) missing values Missing
level0Name has 854767 (86.5%) missing values Missing
level1Gid has 855021 (86.5%) missing values Missing
level1Name has 855020 (86.5%) missing values Missing
level2Gid has 859029 (86.9%) missing values Missing
level2Name has 859040 (86.9%) missing values Missing
level3Gid has 953538 (96.5%) missing values Missing
level3Name has 953860 (96.5%) missing values Missing
iucnRedListCategory has 91545 (9.3%) missing values Missing
Unnamed: 223 has 988401 (> 99.9%) missing values Missing
Unnamed: 224 has 988401 (> 99.9%) missing values Missing
Unnamed: 225 has 988402 (100.0%) missing values Missing
Unnamed: 226 has 988402 (100.0%) missing values Missing
Unnamed: 227 has 988402 (100.0%) missing values Missing
Unnamed: 228 has 988401 (> 99.9%) missing values Missing
Unnamed: 229 has 988401 (> 99.9%) missing values Missing
Unnamed: 230 has 988401 (> 99.9%) missing values Missing
Unnamed: 231 has 988401 (> 99.9%) missing values Missing
Unnamed: 232 has 988401 (> 99.9%) missing values Missing
Unnamed: 233 has 988401 (> 99.9%) missing values Missing
Unnamed: 234 has 988401 (> 99.9%) missing values Missing
Unnamed: 235 has 988401 (> 99.9%) missing values Missing
Unnamed: 236 has 988401 (> 99.9%) missing values Missing
Unnamed: 237 has 988401 (> 99.9%) missing values Missing
Unnamed: 238 has 988401 (> 99.9%) missing values Missing
Unnamed: 239 has 988401 (> 99.9%) missing values Missing
Unnamed: 240 has 988402 (100.0%) missing values Missing
Unnamed: 241 has 988401 (> 99.9%) missing values Missing
Unnamed: 242 has 988401 (> 99.9%) missing values Missing
Unnamed: 243 has 988401 (> 99.9%) missing values Missing
Unnamed: 244 has 988401 (> 99.9%) missing values Missing
Unnamed: 245 has 988402 (100.0%) missing values Missing
Unnamed: 246 has 988401 (> 99.9%) missing values Missing
Unnamed: 247 has 988401 (> 99.9%) missing values Missing
Unnamed: 248 has 988401 (> 99.9%) missing values Missing
Unnamed: 249 has 988401 (> 99.9%) missing values Missing
Unnamed: 250 has 988402 (100.0%) missing values Missing
Unnamed: 251 has 988402 (100.0%) missing values Missing
Unnamed: 252 has 988401 (> 99.9%) missing values Missing
Unnamed: 253 has 988401 (> 99.9%) missing values Missing
Unnamed: 254 has 988401 (> 99.9%) missing values Missing
Unnamed: 255 has 988402 (100.0%) missing values Missing
Unnamed: 256 has 988402 (100.0%) missing values Missing
Unnamed: 257 has 988402 (100.0%) missing values Missing
Unnamed: 258 has 988402 (100.0%) missing values Missing
Unnamed: 259 has 988402 (100.0%) missing values Missing
Unnamed: 260 has 988402 (100.0%) missing values Missing
Unnamed: 261 has 988402 (100.0%) missing values Missing
Unnamed: 262 has 988402 (100.0%) missing values Missing
Unnamed: 263 has 988402 (100.0%) missing values Missing
individualCount is highly skewed (γ1 = 197.6065795) Skewed
gbifID has unique values Unique
occurrenceID has unique values Unique
accessRights is an unsupported type, check if it needs cleaning or further analysis Unsupported
bibliographicCitation is an unsupported type, check if it needs cleaning or further analysis Unsupported
language is an unsupported type, check if it needs cleaning or further analysis Unsupported
references is an unsupported type, check if it needs cleaning or further analysis Unsupported
rightsHolder is an unsupported type, check if it needs cleaning or further analysis Unsupported
type is an unsupported type, check if it needs cleaning or further analysis Unsupported
datasetID is an unsupported type, check if it needs cleaning or further analysis Unsupported
ownerInstitutionCode is an unsupported type, check if it needs cleaning or further analysis Unsupported
informationWithheld is an unsupported type, check if it needs cleaning or further analysis Unsupported
dataGeneralizations is an unsupported type, check if it needs cleaning or further analysis Unsupported
dynamicProperties is an unsupported type, check if it needs cleaning or further analysis Unsupported
recordedByID is an unsupported type, check if it needs cleaning or further analysis Unsupported
organismQuantity is an unsupported type, check if it needs cleaning or further analysis Unsupported
organismQuantityType is an unsupported type, check if it needs cleaning or further analysis Unsupported
sex is an unsupported type, check if it needs cleaning or further analysis Unsupported
reproductiveCondition is an unsupported type, check if it needs cleaning or further analysis Unsupported
caste is an unsupported type, check if it needs cleaning or further analysis Unsupported
behavior is an unsupported type, check if it needs cleaning or further analysis Unsupported
vitality is an unsupported type, check if it needs cleaning or further analysis Unsupported
establishmentMeans is an unsupported type, check if it needs cleaning or further analysis Unsupported
degreeOfEstablishment is an unsupported type, check if it needs cleaning or further analysis Unsupported
pathway is an unsupported type, check if it needs cleaning or further analysis Unsupported
georeferenceVerificationStatus is an unsupported type, check if it needs cleaning or further analysis Unsupported
disposition is an unsupported type, check if it needs cleaning or further analysis Unsupported
associatedOccurrences is an unsupported type, check if it needs cleaning or further analysis Unsupported
associatedReferences is an unsupported type, check if it needs cleaning or further analysis Unsupported
associatedTaxa is an unsupported type, check if it needs cleaning or further analysis Unsupported
otherCatalogNumbers is an unsupported type, check if it needs cleaning or further analysis Unsupported
organismID is an unsupported type, check if it needs cleaning or further analysis Unsupported
organismName is an unsupported type, check if it needs cleaning or further analysis Unsupported
organismScope is an unsupported type, check if it needs cleaning or further analysis Unsupported
associatedOrganisms is an unsupported type, check if it needs cleaning or further analysis Unsupported
previousIdentifications is an unsupported type, check if it needs cleaning or further analysis Unsupported
organismRemarks is an unsupported type, check if it needs cleaning or further analysis Unsupported
materialEntityID is an unsupported type, check if it needs cleaning or further analysis Unsupported
materialEntityRemarks is an unsupported type, check if it needs cleaning or further analysis Unsupported
verbatimLabel is an unsupported type, check if it needs cleaning or further analysis Unsupported
materialSampleID is an unsupported type, check if it needs cleaning or further analysis Unsupported
eventID is an unsupported type, check if it needs cleaning or further analysis Unsupported
parentEventID is an unsupported type, check if it needs cleaning or further analysis Unsupported
eventType is an unsupported type, check if it needs cleaning or further analysis Unsupported
eventTime is an unsupported type, check if it needs cleaning or further analysis Unsupported
samplingProtocol is an unsupported type, check if it needs cleaning or further analysis Unsupported
sampleSizeValue is an unsupported type, check if it needs cleaning or further analysis Unsupported
sampleSizeUnit is an unsupported type, check if it needs cleaning or further analysis Unsupported
samplingEffort is an unsupported type, check if it needs cleaning or further analysis Unsupported
fieldNotes is an unsupported type, check if it needs cleaning or further analysis Unsupported
eventRemarks is an unsupported type, check if it needs cleaning or further analysis Unsupported
higherGeographyID is an unsupported type, check if it needs cleaning or further analysis Unsupported
municipality is an unsupported type, check if it needs cleaning or further analysis Unsupported
verbatimLocality is an unsupported type, check if it needs cleaning or further analysis Unsupported
verbatimElevation is an unsupported type, check if it needs cleaning or further analysis Unsupported
verticalDatum is an unsupported type, check if it needs cleaning or further analysis Unsupported
minimumDistanceAboveSurfaceInMeters is an unsupported type, check if it needs cleaning or further analysis Unsupported
maximumDistanceAboveSurfaceInMeters is an unsupported type, check if it needs cleaning or further analysis Unsupported
locationAccordingTo is an unsupported type, check if it needs cleaning or further analysis Unsupported
locationRemarks is an unsupported type, check if it needs cleaning or further analysis Unsupported
coordinatePrecision is an unsupported type, check if it needs cleaning or further analysis Unsupported
pointRadiusSpatialFit is an unsupported type, check if it needs cleaning or further analysis Unsupported
footprintWKT is an unsupported type, check if it needs cleaning or further analysis Unsupported
geologicalContextID is an unsupported type, check if it needs cleaning or further analysis Unsupported
earliestEonOrLowestEonothem is an unsupported type, check if it needs cleaning or further analysis Unsupported
latestEonOrHighestEonothem is an unsupported type, check if it needs cleaning or further analysis Unsupported
earliestEraOrLowestErathem is an unsupported type, check if it needs cleaning or further analysis Unsupported
latestEraOrHighestErathem is an unsupported type, check if it needs cleaning or further analysis Unsupported
earliestPeriodOrLowestSystem is an unsupported type, check if it needs cleaning or further analysis Unsupported
latestPeriodOrHighestSystem is an unsupported type, check if it needs cleaning or further analysis Unsupported
earliestEpochOrLowestSeries is an unsupported type, check if it needs cleaning or further analysis Unsupported
latestAgeOrHighestStage is an unsupported type, check if it needs cleaning or further analysis Unsupported
highestBiostratigraphicZone is an unsupported type, check if it needs cleaning or further analysis Unsupported
formation is an unsupported type, check if it needs cleaning or further analysis Unsupported
member is an unsupported type, check if it needs cleaning or further analysis Unsupported
identificationID is an unsupported type, check if it needs cleaning or further analysis Unsupported
verbatimIdentification is an unsupported type, check if it needs cleaning or further analysis Unsupported
identifiedByID is an unsupported type, check if it needs cleaning or further analysis Unsupported
scientificNameID is an unsupported type, check if it needs cleaning or further analysis Unsupported
acceptedNameUsageID is an unsupported type, check if it needs cleaning or further analysis Unsupported
parentNameUsageID is an unsupported type, check if it needs cleaning or further analysis Unsupported
originalNameUsageID is an unsupported type, check if it needs cleaning or further analysis Unsupported
nameAccordingToID is an unsupported type, check if it needs cleaning or further analysis Unsupported
acceptedNameUsage is an unsupported type, check if it needs cleaning or further analysis Unsupported
nameAccordingTo is an unsupported type, check if it needs cleaning or further analysis Unsupported
namePublishedInYear is an unsupported type, check if it needs cleaning or further analysis Unsupported
subgenus is an unsupported type, check if it needs cleaning or further analysis Unsupported
vernacularName is an unsupported type, check if it needs cleaning or further analysis Unsupported
elevation is an unsupported type, check if it needs cleaning or further analysis Unsupported
elevationAccuracy is an unsupported type, check if it needs cleaning or further analysis Unsupported
depth is an unsupported type, check if it needs cleaning or further analysis Unsupported
distanceFromCentroidInMeters is an unsupported type, check if it needs cleaning or further analysis Unsupported
hasCoordinate is an unsupported type, check if it needs cleaning or further analysis Unsupported
acceptedTaxonKey is an unsupported type, check if it needs cleaning or further analysis Unsupported
kingdomKey is an unsupported type, check if it needs cleaning or further analysis Unsupported
phylumKey is an unsupported type, check if it needs cleaning or further analysis Unsupported
orderKey is an unsupported type, check if it needs cleaning or further analysis Unsupported
familyKey is an unsupported type, check if it needs cleaning or further analysis Unsupported
genusKey is an unsupported type, check if it needs cleaning or further analysis Unsupported
speciesKey is an unsupported type, check if it needs cleaning or further analysis Unsupported
typifiedName is an unsupported type, check if it needs cleaning or further analysis Unsupported
relativeOrganismQuantity is an unsupported type, check if it needs cleaning or further analysis Unsupported
isSequenced is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 225 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 226 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 227 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 240 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 245 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 250 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 251 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 255 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 256 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 257 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 258 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 259 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 260 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 261 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 262 is an unsupported type, check if it needs cleaning or further analysis Unsupported
Unnamed: 263 is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2025-01-07 15:42:33.680150
Analysis finished2025-01-07 15:43:16.114129
Duration42.43 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

gbifID
Real number (ℝ)

Unique 

Distinct988402
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1968702484
Minimum1317202451
Maximum4987328265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:16.160129image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1317202451
5-th percentile1318018329
Q11321138632
median1675840797
Q32452252006
95-th percentile3467318357
Maximum4987328265
Range3670125814
Interquartile range (IQR)1131113374

Descriptive statistics

Standard deviation754240373
Coefficient of variation (CV)0.3831154678
Kurtosis0.3551417098
Mean1968702484
Median Absolute Deviation (MAD)357148497
Skewness1.062447106
Sum1.945869472 × 1015
Variance5.688785402 × 1017
MonotonicityNot monotonic
2025-01-07T10:43:16.227641image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1675920986 1
 
< 0.1%
1320179379 1
 
< 0.1%
2643346688 1
 
< 0.1%
1702805013 1
 
< 0.1%
2592255053 1
 
< 0.1%
1456226436 1
 
< 0.1%
1320271415 1
 
< 0.1%
3311123372 1
 
< 0.1%
1320270931 1
 
< 0.1%
3897771795 1
 
< 0.1%
Other values (988392) 988392
> 99.9%
ValueCountFrequency (%)
1317202451 1
< 0.1%
1317202507 1
< 0.1%
1317202512 1
< 0.1%
1317202538 1
< 0.1%
1317202545 1
< 0.1%
ValueCountFrequency (%)
4987328265 1
< 0.1%
4987328231 1
< 0.1%
4987328126 1
< 0.1%
4987328109 1
< 0.1%
4987327950 1
< 0.1%

accessRights
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

bibliographicCitation
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

language
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

license
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:16.267647image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters6918814
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCC0_1_0
2nd rowCC0_1_0
3rd rowCC0_1_0
4th rowCC0_1_0
5th rowCC0_1_0
ValueCountFrequency (%)
cc0_1_0 988402
100.0%
2025-01-07T10:43:16.357666image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 1976804
28.6%
0 1976804
28.6%
_ 1976804
28.6%
1 988402
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6918814
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 1976804
28.6%
0 1976804
28.6%
_ 1976804
28.6%
1 988402
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6918814
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 1976804
28.6%
0 1976804
28.6%
_ 1976804
28.6%
1 988402
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6918814
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 1976804
28.6%
0 1976804
28.6%
_ 1976804
28.6%
1 988402
14.3%
Distinct103380
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:16.491843image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters19768040
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39084 ?
Unique (%)4.0%

Sample

1st row2016-08-30T13:42:00Z
2nd row2022-10-26T17:57:00Z
3rd row2020-05-10T23:06:00Z
4th row2020-04-09T11:53:00Z
5th row2021-09-10T21:16:00Z
ValueCountFrequency (%)
2024-10-17t09:48:00z 1536
 
0.2%
2024-10-17t09:52:00z 1531
 
0.2%
2024-10-17t09:51:00z 1451
 
0.1%
2024-10-17t09:55:00z 1419
 
0.1%
2024-10-17t09:49:00z 1377
 
0.1%
2024-10-17t09:50:00z 1314
 
0.1%
2024-10-17t09:53:00z 1255
 
0.1%
2024-10-17t09:54:00z 1248
 
0.1%
2024-10-17t09:57:00z 1194
 
0.1%
2024-10-17t09:56:00z 1136
 
0.1%
Other values (103370) 974941
98.6%
2025-01-07T10:43:16.683927image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 5048546
25.5%
2 2613229
13.2%
1 2474741
12.5%
- 1976804
 
10.0%
: 1976804
 
10.0%
T 988402
 
5.0%
Z 988402
 
5.0%
3 648649
 
3.3%
8 570563
 
2.9%
9 562325
 
2.8%
Other values (4) 1919575
 
9.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19768040
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5048546
25.5%
2 2613229
13.2%
1 2474741
12.5%
- 1976804
 
10.0%
: 1976804
 
10.0%
T 988402
 
5.0%
Z 988402
 
5.0%
3 648649
 
3.3%
8 570563
 
2.9%
9 562325
 
2.8%
Other values (4) 1919575
 
9.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19768040
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5048546
25.5%
2 2613229
13.2%
1 2474741
12.5%
- 1976804
 
10.0%
: 1976804
 
10.0%
T 988402
 
5.0%
Z 988402
 
5.0%
3 648649
 
3.3%
8 570563
 
2.9%
9 562325
 
2.8%
Other values (4) 1919575
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19768040
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5048546
25.5%
2 2613229
13.2%
1 2474741
12.5%
- 1976804
 
10.0%
: 1976804
 
10.0%
T 988402
 
5.0%
Z 988402
 
5.0%
3 648649
 
3.3%
8 570563
 
2.9%
9 562325
 
2.8%
Other values (4) 1919575
 
9.7%

publisher
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:16.757930image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length59
Median length59
Mean length59
Min length59

Characters and Unicode

Total characters58315718
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNational Museum of Natural History, Smithsonian Institution
2nd rowNational Museum of Natural History, Smithsonian Institution
3rd rowNational Museum of Natural History, Smithsonian Institution
4th rowNational Museum of Natural History, Smithsonian Institution
5th rowNational Museum of Natural History, Smithsonian Institution
ValueCountFrequency (%)
national 988402
14.3%
museum 988402
14.3%
of 988402
14.3%
natural 988402
14.3%
history 988402
14.3%
smithsonian 988402
14.3%
institution 988402
14.3%
2025-01-07T10:43:16.866882image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 6918814
11.9%
i 5930412
10.2%
5930412
10.2%
o 4942010
 
8.5%
a 4942010
 
8.5%
n 4942010
 
8.5%
s 3953608
 
6.8%
u 3953608
 
6.8%
N 1976804
 
3.4%
m 1976804
 
3.4%
Other values (11) 12849226
22.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58315718
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 6918814
11.9%
i 5930412
10.2%
5930412
10.2%
o 4942010
 
8.5%
a 4942010
 
8.5%
n 4942010
 
8.5%
s 3953608
 
6.8%
u 3953608
 
6.8%
N 1976804
 
3.4%
m 1976804
 
3.4%
Other values (11) 12849226
22.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58315718
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 6918814
11.9%
i 5930412
10.2%
5930412
10.2%
o 4942010
 
8.5%
a 4942010
 
8.5%
n 4942010
 
8.5%
s 3953608
 
6.8%
u 3953608
 
6.8%
N 1976804
 
3.4%
m 1976804
 
3.4%
Other values (11) 12849226
22.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58315718
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 6918814
11.9%
i 5930412
10.2%
5930412
10.2%
o 4942010
 
8.5%
a 4942010
 
8.5%
n 4942010
 
8.5%
s 3953608
 
6.8%
u 3953608
 
6.8%
N 1976804
 
3.4%
m 1976804
 
3.4%
Other values (11) 12849226
22.0%

references
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

rightsHolder
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

type
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

institutionID
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:16.933387image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length29
Median length29
Mean length29
Min length29

Characters and Unicode

Total characters28663658
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowurn:lsid:biocol.org:col:15463
2nd rowurn:lsid:biocol.org:col:15463
3rd rowurn:lsid:biocol.org:col:15463
4th rowurn:lsid:biocol.org:col:15463
5th rowurn:lsid:biocol.org:col:15463
ValueCountFrequency (%)
urn:lsid:biocol.org:col:15463 988402
100.0%
2025-01-07T10:43:17.037851image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 3953608
13.8%
: 3953608
13.8%
l 2965206
 
10.3%
r 1976804
 
6.9%
c 1976804
 
6.9%
i 1976804
 
6.9%
u 988402
 
3.4%
s 988402
 
3.4%
d 988402
 
3.4%
n 988402
 
3.4%
Other values (8) 7907216
27.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 28663658
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 3953608
13.8%
: 3953608
13.8%
l 2965206
 
10.3%
r 1976804
 
6.9%
c 1976804
 
6.9%
i 1976804
 
6.9%
u 988402
 
3.4%
s 988402
 
3.4%
d 988402
 
3.4%
n 988402
 
3.4%
Other values (8) 7907216
27.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 28663658
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 3953608
13.8%
: 3953608
13.8%
l 2965206
 
10.3%
r 1976804
 
6.9%
c 1976804
 
6.9%
i 1976804
 
6.9%
u 988402
 
3.4%
s 988402
 
3.4%
d 988402
 
3.4%
n 988402
 
3.4%
Other values (8) 7907216
27.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 28663658
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 3953608
13.8%
: 3953608
13.8%
l 2965206
 
10.3%
r 1976804
 
6.9%
c 1976804
 
6.9%
i 1976804
 
6.9%
u 988402
 
3.4%
s 988402
 
3.4%
d 988402
 
3.4%
n 988402
 
3.4%
Other values (8) 7907216
27.6%

collectionID
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:17.092862image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length45
Median length45
Mean length45
Min length45

Characters and Unicode

Total characters44478090
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowurn:uuid:60e28f81-e634-4869-aa3e-732caed713c8
2nd rowurn:uuid:60e28f81-e634-4869-aa3e-732caed713c8
3rd rowurn:uuid:60e28f81-e634-4869-aa3e-732caed713c8
4th rowurn:uuid:60e28f81-e634-4869-aa3e-732caed713c8
5th rowurn:uuid:60e28f81-e634-4869-aa3e-732caed713c8
ValueCountFrequency (%)
urn:uuid:60e28f81-e634-4869-aa3e-732caed713c8 988402
100.0%
2025-01-07T10:43:17.198169image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 3953608
 
8.9%
3 3953608
 
8.9%
e 3953608
 
8.9%
8 3953608
 
8.9%
6 2965206
 
6.7%
a 2965206
 
6.7%
u 2965206
 
6.7%
: 1976804
 
4.4%
4 1976804
 
4.4%
7 1976804
 
4.4%
Other values (10) 13837628
31.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 44478090
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 3953608
 
8.9%
3 3953608
 
8.9%
e 3953608
 
8.9%
8 3953608
 
8.9%
6 2965206
 
6.7%
a 2965206
 
6.7%
u 2965206
 
6.7%
: 1976804
 
4.4%
4 1976804
 
4.4%
7 1976804
 
4.4%
Other values (10) 13837628
31.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 44478090
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 3953608
 
8.9%
3 3953608
 
8.9%
e 3953608
 
8.9%
8 3953608
 
8.9%
6 2965206
 
6.7%
a 2965206
 
6.7%
u 2965206
 
6.7%
: 1976804
 
4.4%
4 1976804
 
4.4%
7 1976804
 
4.4%
Other values (10) 13837628
31.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 44478090
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 3953608
 
8.9%
3 3953608
 
8.9%
e 3953608
 
8.9%
8 3953608
 
8.9%
6 2965206
 
6.7%
a 2965206
 
6.7%
u 2965206
 
6.7%
: 1976804
 
4.4%
4 1976804
 
4.4%
7 1976804
 
4.4%
Other values (10) 13837628
31.1%

datasetID
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

institutionCode
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:17.237169image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1976804
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowUS
5th rowUS
ValueCountFrequency (%)
us 988402
100.0%
2025-01-07T10:43:17.327327image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 988402
50.0%
S 988402
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1976804
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 988402
50.0%
S 988402
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1976804
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 988402
50.0%
S 988402
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1976804
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 988402
50.0%
S 988402
50.0%

collectionCode
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:17.369330image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1976804
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowUS
5th rowUS
ValueCountFrequency (%)
us 988402
100.0%
2025-01-07T10:43:17.456980image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 988402
50.0%
S 988402
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1976804
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 988402
50.0%
S 988402
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1976804
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 988402
50.0%
S 988402
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1976804
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 988402
50.0%
S 988402
50.0%

datasetName
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:17.499854image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters18779638
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNMNH Extant Biology
2nd rowNMNH Extant Biology
3rd rowNMNH Extant Biology
4th rowNMNH Extant Biology
5th rowNMNH Extant Biology
ValueCountFrequency (%)
nmnh 988402
33.3%
extant 988402
33.3%
biology 988402
33.3%
2025-01-07T10:43:17.594746image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 1976804
 
10.5%
t 1976804
 
10.5%
1976804
 
10.5%
o 1976804
 
10.5%
H 988402
 
5.3%
E 988402
 
5.3%
M 988402
 
5.3%
x 988402
 
5.3%
a 988402
 
5.3%
B 988402
 
5.3%
Other values (5) 4942010
26.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18779638
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1976804
 
10.5%
t 1976804
 
10.5%
1976804
 
10.5%
o 1976804
 
10.5%
H 988402
 
5.3%
E 988402
 
5.3%
M 988402
 
5.3%
x 988402
 
5.3%
a 988402
 
5.3%
B 988402
 
5.3%
Other values (5) 4942010
26.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18779638
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1976804
 
10.5%
t 1976804
 
10.5%
1976804
 
10.5%
o 1976804
 
10.5%
H 988402
 
5.3%
E 988402
 
5.3%
M 988402
 
5.3%
x 988402
 
5.3%
a 988402
 
5.3%
B 988402
 
5.3%
Other values (5) 4942010
26.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18779638
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1976804
 
10.5%
t 1976804
 
10.5%
1976804
 
10.5%
o 1976804
 
10.5%
H 988402
 
5.3%
E 988402
 
5.3%
M 988402
 
5.3%
x 988402
 
5.3%
a 988402
 
5.3%
B 988402
 
5.3%
Other values (5) 4942010
26.3%

ownerInstitutionCode
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:17.644747image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length19
Median length18
Mean length18.01104712
Min length18

Characters and Unicode

Total characters17802155
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPRESERVED_SPECIMEN
2nd rowPRESERVED_SPECIMEN
3rd rowPRESERVED_SPECIMEN
4th rowPRESERVED_SPECIMEN
5th rowPRESERVED_SPECIMEN
ValueCountFrequency (%)
preserved_specimen 977483
98.9%
machine_observation 10919
 
1.1%
2025-01-07T10:43:17.749044image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 4909253
27.6%
R 1965885
11.0%
S 1965885
11.0%
P 1954966
 
11.0%
N 999321
 
5.6%
I 999321
 
5.6%
V 988402
 
5.6%
_ 988402
 
5.6%
M 988402
 
5.6%
C 988402
 
5.6%
Other values (6) 1053916
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17802155
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 4909253
27.6%
R 1965885
11.0%
S 1965885
11.0%
P 1954966
 
11.0%
N 999321
 
5.6%
I 999321
 
5.6%
V 988402
 
5.6%
_ 988402
 
5.6%
M 988402
 
5.6%
C 988402
 
5.6%
Other values (6) 1053916
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17802155
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 4909253
27.6%
R 1965885
11.0%
S 1965885
11.0%
P 1954966
 
11.0%
N 999321
 
5.6%
I 999321
 
5.6%
V 988402
 
5.6%
_ 988402
 
5.6%
M 988402
 
5.6%
C 988402
 
5.6%
Other values (6) 1053916
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17802155
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 4909253
27.6%
R 1965885
11.0%
S 1965885
11.0%
P 1954966
 
11.0%
N 999321
 
5.6%
I 999321
 
5.6%
V 988402
 
5.6%
_ 988402
 
5.6%
M 988402
 
5.6%
C 988402
 
5.6%
Other values (6) 1053916
 
5.9%

informationWithheld
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

dataGeneralizations
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

dynamicProperties
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

occurrenceID
Text

Unique 

Distinct988402
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:18.204797image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length63
Median length63
Mean length63
Min length63

Characters and Unicode

Total characters62269326
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique988402 ?
Unique (%)100.0%

Sample

1st rowhttp://n2t.net/ark:/65665/383aab1ce-8b35-4007-8eba-472b592b7a99
2nd rowhttp://n2t.net/ark:/65665/3c8351e79-8b3b-4df0-80be-cb019ba60185
3rd rowhttp://n2t.net/ark:/65665/3c8377593-a51b-4b6a-835d-649053b2ef0f
4th rowhttp://n2t.net/ark:/65665/383b388e9-b7cc-4b41-95cc-e0a1b092179a
5th rowhttp://n2t.net/ark:/65665/3c83e5abc-b64e-45a4-aa42-faf5abc93792
ValueCountFrequency (%)
http://n2t.net/ark:/65665/3c8377593-a51b-4b6a-835d-649053b2ef0f 1
 
< 0.1%
http://n2t.net/ark:/65665/387f5ceac-357c-45e7-909a-711b8beb6942 1
 
< 0.1%
http://n2t.net/ark:/65665/387d0d412-3ba5-4604-8616-79eb46887d63 1
 
< 0.1%
http://n2t.net/ark:/65665/387d51e0b-9b1c-475e-a133-12f665c01b45 1
 
< 0.1%
http://n2t.net/ark:/65665/387d71aa4-7aa8-4ea2-b37e-e4316385f771 1
 
< 0.1%
http://n2t.net/ark:/65665/387d80e6f-9bab-4af0-92a5-986d43bcceb2 1
 
< 0.1%
http://n2t.net/ark:/65665/387d9dcbc-a22f-45c5-9414-9524ff13bf1b 1
 
< 0.1%
http://n2t.net/ark:/65665/387dec6ad-d4ad-452e-b560-85f693ade49c 1
 
< 0.1%
http://n2t.net/ark:/65665/387e03637-4f30-407f-8e66-6910426af158 1
 
< 0.1%
http://n2t.net/ark:/65665/387e2f046-8d0d-494f-b87c-f8c2a84d8aa0 1
 
< 0.1%
Other values (988392) 988392
> 99.9%
2025-01-07T10:43:18.758872image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 4942010
 
7.9%
6 4818872
 
7.7%
- 3953608
 
6.3%
t 3953608
 
6.3%
5 3831724
 
6.2%
a 3087982
 
5.0%
4 2844631
 
4.6%
e 2842218
 
4.6%
2 2841949
 
4.6%
3 2841551
 
4.6%
Other values (16) 26311173
42.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 62269326
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
/ 4942010
 
7.9%
6 4818872
 
7.7%
- 3953608
 
6.3%
t 3953608
 
6.3%
5 3831724
 
6.2%
a 3087982
 
5.0%
4 2844631
 
4.6%
e 2842218
 
4.6%
2 2841949
 
4.6%
3 2841551
 
4.6%
Other values (16) 26311173
42.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 62269326
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
/ 4942010
 
7.9%
6 4818872
 
7.7%
- 3953608
 
6.3%
t 3953608
 
6.3%
5 3831724
 
6.2%
a 3087982
 
5.0%
4 2844631
 
4.6%
e 2842218
 
4.6%
2 2841949
 
4.6%
3 2841551
 
4.6%
Other values (16) 26311173
42.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 62269326
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
/ 4942010
 
7.9%
6 4818872
 
7.7%
- 3953608
 
6.3%
t 3953608
 
6.3%
5 3831724
 
6.2%
a 3087982
 
5.0%
4 2844631
 
4.6%
e 2842218
 
4.6%
2 2841949
 
4.6%
3 2841551
 
4.6%
Other values (16) 26311173
42.3%

catalogNumber
Text

Missing 

Distinct843685
Distinct (%)98.6%
Missing132504
Missing (%)13.4%
Memory size7.5 MiB
2025-01-07T10:43:19.321939image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length22
Median length10
Mean length9.636065279
Min length4

Characters and Unicode

Total characters8247489
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique832040 ?
Unique (%)97.2%

Sample

1st rowUS 213621
2nd rowUS 2144946
3rd rowUS 3113222
4th rowUS 2583825
5th rowUS 3026466
ValueCountFrequency (%)
us 846588
49.7%
sem 52
 
< 0.1%
1 35
 
< 0.1%
27
 
< 0.1%
micrograph 26
 
< 0.1%
stub 26
 
< 0.1%
3 15
 
< 0.1%
2 13
 
< 0.1%
169920 12
 
< 0.1%
95340 9
 
< 0.1%
Other values (843649) 855865
50.3%
2025-01-07T10:43:19.941097image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 855977
10.4%
U 855899
10.4%
846770
10.3%
2 752400
9.1%
1 735998
8.9%
3 670736
8.1%
5 512863
 
6.2%
4 511349
 
6.2%
6 510981
 
6.2%
7 501756
 
6.1%
Other values (36) 1492760
18.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8247489
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 855977
10.4%
U 855899
10.4%
846770
10.3%
2 752400
9.1%
1 735998
8.9%
3 670736
8.1%
5 512863
 
6.2%
4 511349
 
6.2%
6 510981
 
6.2%
7 501756
 
6.1%
Other values (36) 1492760
18.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8247489
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 855977
10.4%
U 855899
10.4%
846770
10.3%
2 752400
9.1%
1 735998
8.9%
3 670736
8.1%
5 512863
 
6.2%
4 511349
 
6.2%
6 510981
 
6.2%
7 501756
 
6.1%
Other values (36) 1492760
18.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8247489
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 855977
10.4%
U 855899
10.4%
846770
10.3%
2 752400
9.1%
1 735998
8.9%
3 670736
8.1%
5 512863
 
6.2%
4 511349
 
6.2%
6 510981
 
6.2%
7 501756
 
6.1%
Other values (36) 1492760
18.1%
Distinct163293
Distinct (%)16.7%
Missing8698
Missing (%)0.9%
Memory size7.5 MiB
2025-01-07T10:43:20.165684image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length90
Median length72
Mean length4.48941415
Min length1

Characters and Unicode

Total characters4398297
Distinct characters109
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique114501 ?
Unique (%)11.7%

Sample

1st rowBLM-210-IV-11-B-TDS
2nd row4319
3rd row2429
4th row95426
5th row1414/512
ValueCountFrequency (%)
s.n 141397
 
13.6%
bureau 4447
 
0.4%
eyd 3365
 
0.3%
s 3110
 
0.3%
n 3006
 
0.3%
of 2991
 
0.3%
science 2898
 
0.3%
d&ml 2806
 
0.3%
2716
 
0.3%
h 1941
 
0.2%
Other values (128797) 872266
83.8%
2025-01-07T10:43:20.446409image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 527431
12.0%
2 407354
9.3%
3 350940
 
8.0%
4 329711
 
7.5%
5 316988
 
7.2%
0 316877
 
7.2%
6 306238
 
7.0%
. 298537
 
6.8%
7 287805
 
6.5%
8 276699
 
6.3%
Other values (99) 979717
22.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4398297
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 527431
12.0%
2 407354
9.3%
3 350940
 
8.0%
4 329711
 
7.5%
5 316988
 
7.2%
0 316877
 
7.2%
6 306238
 
7.0%
. 298537
 
6.8%
7 287805
 
6.5%
8 276699
 
6.3%
Other values (99) 979717
22.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4398297
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 527431
12.0%
2 407354
9.3%
3 350940
 
8.0%
4 329711
 
7.5%
5 316988
 
7.2%
0 316877
 
7.2%
6 306238
 
7.0%
. 298537
 
6.8%
7 287805
 
6.5%
8 276699
 
6.3%
Other values (99) 979717
22.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4398297
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 527431
12.0%
2 407354
9.3%
3 350940
 
8.0%
4 329711
 
7.5%
5 316988
 
7.2%
0 316877
 
7.2%
6 306238
 
7.0%
. 298537
 
6.8%
7 287805
 
6.5%
8 276699
 
6.3%
Other values (99) 979717
22.3%

recordedBy
Text

Missing 

Distinct71729
Distinct (%)7.3%
Missing11879
Missing (%)1.2%
Memory size7.5 MiB
2025-01-07T10:43:20.664293image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length201
Median length155
Mean length17.24259848
Min length1

Characters and Unicode

Total characters16837794
Distinct characters140
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36179 ?
Unique (%)3.7%

Sample

1st rowContinental Shelf Associates for the MMS/BLM
2nd rowJ. Soukup
3rd rowI. Morel
4th rowJ. Steyermark & Cora Steyermark
5th rowA. Oakes & -. Ellis
ValueCountFrequency (%)
273336
 
7.3%
j 195095
 
5.2%
a 167294
 
4.5%
r 148560
 
4.0%
e 148212
 
4.0%
c 138644
 
3.7%
m 133736
 
3.6%
h 120329
 
3.2%
l 97924
 
2.6%
w 96924
 
2.6%
Other values (28460) 2203562
59.2%
2025-01-07T10:43:20.942264image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2747093
16.3%
. 2002049
 
11.9%
e 1082546
 
6.4%
r 792877
 
4.7%
a 787773
 
4.7%
o 664253
 
3.9%
n 660896
 
3.9%
l 631169
 
3.7%
i 544852
 
3.2%
t 439669
 
2.6%
Other values (130) 6484617
38.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16837794
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2747093
16.3%
. 2002049
 
11.9%
e 1082546
 
6.4%
r 792877
 
4.7%
a 787773
 
4.7%
o 664253
 
3.9%
n 660896
 
3.9%
l 631169
 
3.7%
i 544852
 
3.2%
t 439669
 
2.6%
Other values (130) 6484617
38.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16837794
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2747093
16.3%
. 2002049
 
11.9%
e 1082546
 
6.4%
r 792877
 
4.7%
a 787773
 
4.7%
o 664253
 
3.9%
n 660896
 
3.9%
l 631169
 
3.7%
i 544852
 
3.2%
t 439669
 
2.6%
Other values (130) 6484617
38.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16837794
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2747093
16.3%
. 2002049
 
11.9%
e 1082546
 
6.4%
r 792877
 
4.7%
a 787773
 
4.7%
o 664253
 
3.9%
n 660896
 
3.9%
l 631169
 
3.7%
i 544852
 
3.2%
t 439669
 
2.6%
Other values (130) 6484617
38.5%

recordedByID
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

individualCount
Real number (ℝ)

Skewed 

Distinct18
Distinct (%)< 0.1%
Missing117
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.000536283
Minimum0
Maximum26
Zeros64
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:21.014356image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum26
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.06333012863
Coefficient of variation (CV)0.06329618399
Kurtosis54631.12718
Mean1.000536283
Median Absolute Deviation (MAD)0
Skewness197.6065795
Sum988815
Variance0.004010705192
MonotonicityNot monotonic
2025-01-07T10:43:21.064355image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 988001
> 99.9%
2 114
 
< 0.1%
0 64
 
< 0.1%
3 35
 
< 0.1%
4 26
 
< 0.1%
5 14
 
< 0.1%
6 8
 
< 0.1%
9 5
 
< 0.1%
7 5
 
< 0.1%
11 3
 
< 0.1%
Other values (8) 10
 
< 0.1%
(Missing) 117
 
< 0.1%
ValueCountFrequency (%)
0 64
 
< 0.1%
1 988001
> 99.9%
2 114
 
< 0.1%
3 35
 
< 0.1%
4 26
 
< 0.1%
ValueCountFrequency (%)
26 1
< 0.1%
21 1
< 0.1%
19 1
< 0.1%
18 1
< 0.1%
14 1
< 0.1%

organismQuantity
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

organismQuantityType
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

sex
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

lifeStage
Text

Missing 

Distinct3
Distinct (%)< 0.1%
Missing916836
Missing (%)92.8%
Memory size7.5 MiB
2025-01-07T10:43:21.163749image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length9
Mean length8.755330744
Min length8

Characters and Unicode

Total characters626584
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFruiting
2nd rowFlowering
3rd rowFlowering
4th rowFlowering
5th rowFlowering
ValueCountFrequency (%)
flowering 43566
60.9%
fruiting 22755
31.8%
vegetative 5245
 
7.3%
2025-01-07T10:43:21.266876image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 94321
15.1%
g 71566
11.4%
F 66321
10.6%
n 66321
10.6%
r 66321
10.6%
e 59301
9.5%
o 43566
7.0%
w 43566
7.0%
l 43566
7.0%
t 33245
 
5.3%
Other values (4) 38490
6.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 626584
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 94321
15.1%
g 71566
11.4%
F 66321
10.6%
n 66321
10.6%
r 66321
10.6%
e 59301
9.5%
o 43566
7.0%
w 43566
7.0%
l 43566
7.0%
t 33245
 
5.3%
Other values (4) 38490
6.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 626584
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 94321
15.1%
g 71566
11.4%
F 66321
10.6%
n 66321
10.6%
r 66321
10.6%
e 59301
9.5%
o 43566
7.0%
w 43566
7.0%
l 43566
7.0%
t 33245
 
5.3%
Other values (4) 38490
6.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 626584
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 94321
15.1%
g 71566
11.4%
F 66321
10.6%
n 66321
10.6%
r 66321
10.6%
e 59301
9.5%
o 43566
7.0%
w 43566
7.0%
l 43566
7.0%
t 33245
 
5.3%
Other values (4) 38490
6.1%

reproductiveCondition
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

caste
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

behavior
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

vitality
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

establishmentMeans
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

degreeOfEstablishment
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

pathway
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

georeferenceVerificationStatus
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

occurrenceStatus
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:21.309166image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters6918814
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPRESENT
2nd rowPRESENT
3rd rowPRESENT
4th rowPRESENT
5th rowPRESENT
ValueCountFrequency (%)
present 988402
100.0%
2025-01-07T10:43:21.397038image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 1976804
28.6%
P 988402
14.3%
R 988402
14.3%
S 988402
14.3%
N 988402
14.3%
T 988402
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6918814
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 1976804
28.6%
P 988402
14.3%
R 988402
14.3%
S 988402
14.3%
N 988402
14.3%
T 988402
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6918814
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 1976804
28.6%
P 988402
14.3%
R 988402
14.3%
S 988402
14.3%
N 988402
14.3%
T 988402
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6918814
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 1976804
28.6%
P 988402
14.3%
R 988402
14.3%
S 988402
14.3%
N 988402
14.3%
T 988402
14.3%

preparations
Text

Missing 

Distinct77
Distinct (%)0.3%
Missing959242
Missing (%)97.0%
Memory size7.5 MiB
2025-01-07T10:43:21.463039image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length142
Median length94
Mean length13.18954047
Min length3

Characters and Unicode

Total characters384607
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)0.1%

Sample

1st rowWood Sample
2nd rowPhotograph
3rd rowMicroslide
4th rowPhotograph
5th rowPhotograph; Photograph
ValueCountFrequency (%)
wood 9236
18.9%
sample 9236
18.9%
microslide 8980
18.3%
photograph 7481
15.3%
individual 4028
8.2%
strewn 2184
 
4.5%
sem 1492
 
3.0%
micrograph 1411
 
2.9%
ink 1139
 
2.3%
and 637
 
1.3%
Other values (48) 3129
 
6.4%
2025-01-07T10:43:21.603539image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 44561
 
11.6%
i 33828
 
8.8%
d 27045
 
7.0%
a 23965
 
6.2%
l 23811
 
6.2%
r 23041
 
6.0%
e 21867
 
5.7%
19793
 
5.1%
p 18828
 
4.9%
h 16434
 
4.3%
Other values (33) 131434
34.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 384607
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 44561
 
11.6%
i 33828
 
8.8%
d 27045
 
7.0%
a 23965
 
6.2%
l 23811
 
6.2%
r 23041
 
6.0%
e 21867
 
5.7%
19793
 
5.1%
p 18828
 
4.9%
h 16434
 
4.3%
Other values (33) 131434
34.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 384607
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 44561
 
11.6%
i 33828
 
8.8%
d 27045
 
7.0%
a 23965
 
6.2%
l 23811
 
6.2%
r 23041
 
6.0%
e 21867
 
5.7%
19793
 
5.1%
p 18828
 
4.9%
h 16434
 
4.3%
Other values (33) 131434
34.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 384607
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 44561
 
11.6%
i 33828
 
8.8%
d 27045
 
7.0%
a 23965
 
6.2%
l 23811
 
6.2%
r 23041
 
6.0%
e 21867
 
5.7%
19793
 
5.1%
p 18828
 
4.9%
h 16434
 
4.3%
Other values (33) 131434
34.2%

disposition
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

associatedOccurrences
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

associatedReferences
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

associatedSequences
Text

Missing 

Distinct73
Distinct (%)98.6%
Missing988328
Missing (%)> 99.9%
Memory size7.5 MiB
2025-01-07T10:43:21.688758image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length249
Median length199
Mean length146.972973
Min length49

Characters and Unicode

Total characters10876
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique72 ?
Unique (%)97.3%

Sample

1st rowhttps://www.ncbi.nlm.nih.gov/gquery?term=ON553270
2nd rowhttps://www.ncbi.nlm.nih.gov/gquery?term=MT553291
3rd rowhttps://www.ncbi.nlm.nih.gov/gquery?term=MT553246
4th rowhttps://www.ncbi.nlm.nih.gov/gquery?term=MT553283
5th rowhttps://www.ncbi.nlm.nih.gov/gquery?term=EU527211;https://www.ncbi.nlm.nih.gov/gquery?term=EU527308;https://www.ncbi.nlm.nih.gov/gquery?term=EU527261
ValueCountFrequency (%)
https://www.ncbi.nlm.nih.gov/gquery?term=jn837181;https://www.ncbi.nlm.nih.gov/gquery?term=jn837465;https://www.ncbi.nlm.nih.gov/gquery?term=jn837361;https://www.ncbi.nlm.nih.gov/gquery?term=jn837271 2
 
2.7%
https://www.ncbi.nlm.nih.gov/gquery?term=mt553291 1
 
1.4%
https://www.ncbi.nlm.nih.gov/gquery?term=mt553246 1
 
1.4%
https://www.ncbi.nlm.nih.gov/gquery?term=mt553283 1
 
1.4%
https://www.ncbi.nlm.nih.gov/gquery?term=eu527211;https://www.ncbi.nlm.nih.gov/gquery?term=eu527308;https://www.ncbi.nlm.nih.gov/gquery?term=eu527261 1
 
1.4%
https://www.ncbi.nlm.nih.gov/gquery?term=kf989590;https://www.ncbi.nlm.nih.gov/gquery?term=kf989809;https://www.ncbi.nlm.nih.gov/gquery?term=kf990009;https://www.ncbi.nlm.nih.gov/gquery?term=kf989698 1
 
1.4%
https://www.ncbi.nlm.nih.gov/gquery?term=jn837113;https://www.ncbi.nlm.nih.gov/gquery?term=jn837294;https://www.ncbi.nlm.nih.gov/gquery?term=jn837203 1
 
1.4%
https://www.ncbi.nlm.nih.gov/gquery?term=kc986936 1
 
1.4%
https://www.ncbi.nlm.nih.gov/gquery?term=on553270 1
 
1.4%
https://www.ncbi.nlm.nih.gov/gquery?term=eu527225;https://www.ncbi.nlm.nih.gov/gquery?term=eu527322;https://www.ncbi.nlm.nih.gov/gquery?term=eu527275 1
 
1.4%
Other values (63) 63
85.1%
2025-01-07T10:43:21.826212image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 876
 
8.1%
t 657
 
6.0%
/ 657
 
6.0%
w 657
 
6.0%
n 657
 
6.0%
h 438
 
4.0%
i 438
 
4.0%
g 438
 
4.0%
m 438
 
4.0%
e 438
 
4.0%
Other values (38) 5182
47.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10876
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 876
 
8.1%
t 657
 
6.0%
/ 657
 
6.0%
w 657
 
6.0%
n 657
 
6.0%
h 438
 
4.0%
i 438
 
4.0%
g 438
 
4.0%
m 438
 
4.0%
e 438
 
4.0%
Other values (38) 5182
47.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10876
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 876
 
8.1%
t 657
 
6.0%
/ 657
 
6.0%
w 657
 
6.0%
n 657
 
6.0%
h 438
 
4.0%
i 438
 
4.0%
g 438
 
4.0%
m 438
 
4.0%
e 438
 
4.0%
Other values (38) 5182
47.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10876
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 876
 
8.1%
t 657
 
6.0%
/ 657
 
6.0%
w 657
 
6.0%
n 657
 
6.0%
h 438
 
4.0%
i 438
 
4.0%
g 438
 
4.0%
m 438
 
4.0%
e 438
 
4.0%
Other values (38) 5182
47.6%

associatedTaxa
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

otherCatalogNumbers
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

occurrenceRemarks
Text

Missing 

Distinct7579
Distinct (%)37.9%
Missing968411
Missing (%)98.0%
Memory size7.5 MiB
2025-01-07T10:43:22.016935image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4263
Median length2214
Mean length78.05767595
Min length1

Characters and Unicode

Total characters1560451
Distinct characters123
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6837 ?
Unique (%)34.2%

Sample

1st rowReceived as: seed
2nd rowTranscribed by digital volunteers
3rd rowBRG
4th rowTranscribed by digital volunteers; Original spelling as annotated and published is "subplebeia". Same (?) taxon re-published in Contr. U.S. Natl. Herb. 17: 46 (1913) with more explicit type citation. Unclear whether Lecidea subplebeia is a later homonym of Lecidea subplebeja Vain. (1890); Lecidea austrocalifornica Zahlbr. published as replacement name but citing Lecidea "subplebeja Nyl. apud Hasse". The latter name is superfluous if the original name is not a later homonym.
5th rowUS, NY
ValueCountFrequency (%)
by 8401
 
3.7%
transcribed 6608
 
2.9%
digital 6534
 
2.9%
volunteers 6533
 
2.9%
4336
 
1.9%
of 3855
 
1.7%
us 3164
 
1.4%
as 3111
 
1.4%
and 2908
 
1.3%
the 2877
 
1.3%
Other values (18932) 177871
78.6%
2025-01-07T10:43:22.293049image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
206207
 
13.2%
e 125859
 
8.1%
a 97880
 
6.3%
i 90227
 
5.8%
t 77282
 
5.0%
n 75398
 
4.8%
o 74864
 
4.8%
r 73280
 
4.7%
l 65793
 
4.2%
s 59459
 
3.8%
Other values (113) 614202
39.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1560451
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
206207
 
13.2%
e 125859
 
8.1%
a 97880
 
6.3%
i 90227
 
5.8%
t 77282
 
5.0%
n 75398
 
4.8%
o 74864
 
4.8%
r 73280
 
4.7%
l 65793
 
4.2%
s 59459
 
3.8%
Other values (113) 614202
39.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1560451
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
206207
 
13.2%
e 125859
 
8.1%
a 97880
 
6.3%
i 90227
 
5.8%
t 77282
 
5.0%
n 75398
 
4.8%
o 74864
 
4.8%
r 73280
 
4.7%
l 65793
 
4.2%
s 59459
 
3.8%
Other values (113) 614202
39.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1560451
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
206207
 
13.2%
e 125859
 
8.1%
a 97880
 
6.3%
i 90227
 
5.8%
t 77282
 
5.0%
n 75398
 
4.8%
o 74864
 
4.8%
r 73280
 
4.7%
l 65793
 
4.2%
s 59459
 
3.8%
Other values (113) 614202
39.4%

organismID
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

organismName
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

organismScope
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

associatedOrganisms
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

previousIdentifications
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

organismRemarks
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

materialEntityID
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

materialEntityRemarks
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

verbatimLabel
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

materialSampleID
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

eventID
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

parentEventID
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

eventType
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

fieldNumber
Text

Missing 

Distinct5
Distinct (%)8.5%
Missing988343
Missing (%)> 99.9%
Memory size7.5 MiB
2025-01-07T10:43:22.354047image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length19
Median length9
Mean length9.322033898
Min length9

Characters and Unicode

Total characters550
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)6.8%

Sample

1st rowSample OY
2nd rowSample OY
3rd rowSample OY
4th rowSample OY
5th rowSample OY
ValueCountFrequency (%)
sample 55
45.8%
oy 55
45.8%
a 2
 
1.7%
u.s 1
 
0.8%
virgin 1
 
0.8%
islands 1
 
0.8%
alakai_220 1
 
0.8%
koolau_784 1
 
0.8%
koolau 1
 
0.8%
850 1
 
0.8%
2025-01-07T10:43:22.451206image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 62
11.3%
61
11.1%
l 59
10.7%
S 56
10.2%
p 55
10.0%
m 55
10.0%
e 55
10.0%
O 55
10.0%
Y 55
10.0%
o 4
 
0.7%
Other values (22) 33
6.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 550
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 62
11.3%
61
11.1%
l 59
10.7%
S 56
10.2%
p 55
10.0%
m 55
10.0%
e 55
10.0%
O 55
10.0%
Y 55
10.0%
o 4
 
0.7%
Other values (22) 33
6.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 550
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 62
11.3%
61
11.1%
l 59
10.7%
S 56
10.2%
p 55
10.0%
m 55
10.0%
e 55
10.0%
O 55
10.0%
Y 55
10.0%
o 4
 
0.7%
Other values (22) 33
6.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 550
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 62
11.3%
61
11.1%
l 59
10.7%
S 56
10.2%
p 55
10.0%
m 55
10.0%
e 55
10.0%
O 55
10.0%
Y 55
10.0%
o 4
 
0.7%
Other values (22) 33
6.0%

eventDate
Text

Missing 

Distinct66958
Distinct (%)7.7%
Missing119809
Missing (%)12.1%
Memory size7.5 MiB
2025-01-07T10:43:22.626175image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length21
Median length10
Mean length10.01351151
Min length4

Characters and Unicode

Total characters8697666
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11342 ?
Unique (%)1.3%

Sample

1st row1981-04-30
2nd row1954-08-07
3rd row1947-04-03
4th row1966-04-01
5th row1971-03-23
ValueCountFrequency (%)
1891 1085
 
0.1%
1923 918
 
0.1%
1922 844
 
0.1%
1889 814
 
0.1%
1885 814
 
0.1%
1892 772
 
0.1%
1890 762
 
0.1%
1897 759
 
0.1%
1880 756
 
0.1%
1875 745
 
0.1%
Other values (66948) 860324
99.0%
2025-01-07T10:43:22.871871image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 1652713
19.0%
1 1649641
19.0%
0 1325270
15.2%
9 1106125
12.7%
2 658306
 
7.6%
8 502398
 
5.8%
7 370750
 
4.3%
6 370722
 
4.3%
3 354814
 
4.1%
5 329527
 
3.8%
Other values (2) 377400
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8697666
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 1652713
19.0%
1 1649641
19.0%
0 1325270
15.2%
9 1106125
12.7%
2 658306
 
7.6%
8 502398
 
5.8%
7 370750
 
4.3%
6 370722
 
4.3%
3 354814
 
4.1%
5 329527
 
3.8%
Other values (2) 377400
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8697666
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 1652713
19.0%
1 1649641
19.0%
0 1325270
15.2%
9 1106125
12.7%
2 658306
 
7.6%
8 502398
 
5.8%
7 370750
 
4.3%
6 370722
 
4.3%
3 354814
 
4.1%
5 329527
 
3.8%
Other values (2) 377400
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8697666
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 1652713
19.0%
1 1649641
19.0%
0 1325270
15.2%
9 1106125
12.7%
2 658306
 
7.6%
8 502398
 
5.8%
7 370750
 
4.3%
6 370722
 
4.3%
3 354814
 
4.1%
5 329527
 
3.8%
Other values (2) 377400
 
4.3%

eventTime
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

startDayOfYear
Real number (ℝ)

Missing 

Distinct366
Distinct (%)0.1%
Missing261666
Missing (%)26.5%
Infinite0
Infinite (%)0.0%
Mean183.1457682
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:22.949544image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile28
Q1115
median190
Q3247
95-th percentile334
Maximum366
Range365
Interquartile range (IQR)132

Descriptive statistics

Standard deviation90.59829284
Coefficient of variation (CV)0.4946786035
Kurtosis-0.7841180101
Mean183.1457682
Median Absolute Deviation (MAD)65
Skewness-0.08672258947
Sum133098623
Variance8208.050666
MonotonicityNot monotonic
2025-01-07T10:43:23.015896image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201 3860
 
0.4%
200 3710
 
0.4%
196 3699
 
0.4%
210 3653
 
0.4%
199 3644
 
0.4%
206 3635
 
0.4%
209 3596
 
0.4%
208 3571
 
0.4%
197 3518
 
0.4%
205 3509
 
0.4%
Other values (356) 690341
69.8%
(Missing) 261666
 
26.5%
ValueCountFrequency (%)
1 1351
0.1%
2 946
0.1%
3 1079
0.1%
4 1059
0.1%
5 1286
0.1%
ValueCountFrequency (%)
366 208
 
< 0.1%
365 907
0.1%
364 1083
0.1%
363 1106
0.1%
362 1218
0.1%

endDayOfYear
Real number (ℝ)

Missing 

Distinct366
Distinct (%)0.1%
Missing261666
Missing (%)26.5%
Infinite0
Infinite (%)0.0%
Mean183.3873277
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:23.082352image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile28
Q1115
median191
Q3247
95-th percentile334
Maximum366
Range365
Interquartile range (IQR)132

Descriptive statistics

Standard deviation90.50922826
Coefficient of variation (CV)0.4935413443
Kurtosis-0.7829490925
Mean183.3873277
Median Absolute Deviation (MAD)65
Skewness-0.08733334367
Sum133274173
Variance8191.9204
MonotonicityNot monotonic
2025-01-07T10:43:23.147111image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201 3878
 
0.4%
200 3781
 
0.4%
210 3758
 
0.4%
199 3668
 
0.4%
206 3643
 
0.4%
196 3642
 
0.4%
209 3624
 
0.4%
208 3616
 
0.4%
197 3589
 
0.4%
205 3564
 
0.4%
Other values (356) 689973
69.8%
(Missing) 261666
 
26.5%
ValueCountFrequency (%)
1 1265
0.1%
2 924
0.1%
3 1101
0.1%
4 1109
0.1%
5 1030
0.1%
ValueCountFrequency (%)
366 243
 
< 0.1%
365 1206
0.1%
364 1176
0.1%
363 1037
0.1%
362 1262
0.1%

year
Real number (ℝ)

Missing 

Distinct250
Distinct (%)< 0.1%
Missing122319
Missing (%)12.4%
Infinite0
Infinite (%)0.0%
Mean1941.22774
Minimum1596
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:23.214418image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1596
5-th percentile1884
Q11913
median1942
Q31969
95-th percentile2000
Maximum2024
Range428
Interquartile range (IQR)56

Descriptive statistics

Standard deviation36.79876441
Coefficient of variation (CV)0.01895643857
Kurtosis-0.4863705183
Mean1941.22774
Median Absolute Deviation (MAD)28
Skewness-0.1578071035
Sum1681264345
Variance1354.149062
MonotonicityNot monotonic
2025-01-07T10:43:23.277634image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1966 11485
 
1.2%
1964 11177
 
1.1%
1939 10631
 
1.1%
1929 9967
 
1.0%
1949 9934
 
1.0%
1938 9757
 
1.0%
1965 9721
 
1.0%
1962 9422
 
1.0%
1922 9238
 
0.9%
1968 9163
 
0.9%
Other values (240) 765588
77.5%
(Missing) 122319
 
12.4%
ValueCountFrequency (%)
1596 1
 
< 0.1%
1634 1
 
< 0.1%
1760 9
< 0.1%
1761 1
 
< 0.1%
1766 2
 
< 0.1%
ValueCountFrequency (%)
2024 71
 
< 0.1%
2023 140
< 0.1%
2022 319
< 0.1%
2021 232
< 0.1%
2020 312
< 0.1%

month
Real number (ℝ)

Missing 

Distinct12
Distinct (%)< 0.1%
Missing181983
Missing (%)18.4%
Infinite0
Infinite (%)0.0%
Mean6.525296403
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:23.328361image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q39
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.9707949
Coefficient of variation (CV)0.4552735565
Kurtosis-0.7852309976
Mean6.525296403
Median Absolute Deviation (MAD)2
Skewness-0.09401272942
Sum5262123
Variance8.82562234
MonotonicityNot monotonic
2025-01-07T10:43:23.376362image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 116222
11.8%
8 105467
10.7%
6 87311
8.8%
5 73413
7.4%
9 72707
 
7.4%
4 61870
 
6.3%
3 56406
 
5.7%
10 54634
 
5.5%
2 49486
 
5.0%
1 45951
 
4.6%
Other values (2) 82952
8.4%
(Missing) 181983
18.4%
ValueCountFrequency (%)
1 45951
4.6%
2 49486
5.0%
3 56406
5.7%
4 61870
6.3%
5 73413
7.4%
ValueCountFrequency (%)
12 39106
 
4.0%
11 43846
4.4%
10 54634
5.5%
9 72707
7.4%
8 105467
10.7%

day
Real number (ℝ)

Missing 

Distinct31
Distinct (%)< 0.1%
Missing314697
Missing (%)31.8%
Infinite0
Infinite (%)0.0%
Mean15.81222494
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:23.425831image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.696018927
Coefficient of variation (CV)0.5499554277
Kurtosis-1.168495604
Mean15.81222494
Median Absolute Deviation (MAD)7
Skewness-0.01503161712
Sum10652775
Variance75.62074519
MonotonicityNot monotonic
2025-01-07T10:43:23.602042image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20 24963
 
2.5%
15 24514
 
2.5%
18 23599
 
2.4%
10 23434
 
2.4%
19 22891
 
2.3%
25 22886
 
2.3%
17 22629
 
2.3%
23 22542
 
2.3%
24 22331
 
2.3%
21 22292
 
2.3%
Other values (21) 441624
44.7%
(Missing) 314697
31.8%
ValueCountFrequency (%)
1 21533
2.2%
2 21086
2.1%
3 21038
2.1%
4 21265
2.2%
5 21671
2.2%
ValueCountFrequency (%)
31 11367
1.2%
30 20168
2.0%
29 19865
2.0%
28 21904
2.2%
27 22214
2.2%

verbatimEventDate
Text

Missing 

Distinct83121
Distinct (%)25.0%
Missing655426
Missing (%)66.3%
Memory size7.5 MiB
2025-01-07T10:43:23.791165image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length69610
Median length11
Mean length13.58990137
Min length1

Characters and Unicode

Total characters4525111
Distinct characters101
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34904 ?
Unique (%)10.5%

Sample

1st row30 Apr 1981
2nd row16 Dec 1953
3rd row-- --- ----
4th row01 Feb 1974
5th rowTranscribed d/m/y: 28/4/76
ValueCountFrequency (%)
124747
 
12.0%
transcribed 35815
 
3.5%
d/m/y 35815
 
3.5%
jul 29191
 
2.8%
aug 27927
 
2.7%
may 22223
 
2.1%
sep 22121
 
2.1%
jun 22087
 
2.1%
to 19593
 
1.9%
apr 19397
 
1.9%
Other values (27964) 676941
65.4%
2025-01-07T10:43:24.072726image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
698931
 
15.4%
1 452053
 
10.0%
- 374626
 
8.3%
9 327456
 
7.2%
2 199891
 
4.4%
0 167707
 
3.7%
8 147074
 
3.3%
/ 146437
 
3.2%
r 129266
 
2.9%
e 109856
 
2.4%
Other values (91) 1771814
39.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4525111
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
698931
 
15.4%
1 452053
 
10.0%
- 374626
 
8.3%
9 327456
 
7.2%
2 199891
 
4.4%
0 167707
 
3.7%
8 147074
 
3.3%
/ 146437
 
3.2%
r 129266
 
2.9%
e 109856
 
2.4%
Other values (91) 1771814
39.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4525111
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
698931
 
15.4%
1 452053
 
10.0%
- 374626
 
8.3%
9 327456
 
7.2%
2 199891
 
4.4%
0 167707
 
3.7%
8 147074
 
3.3%
/ 146437
 
3.2%
r 129266
 
2.9%
e 109856
 
2.4%
Other values (91) 1771814
39.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4525111
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
698931
 
15.4%
1 452053
 
10.0%
- 374626
 
8.3%
9 327456
 
7.2%
2 199891
 
4.4%
0 167707
 
3.7%
8 147074
 
3.3%
/ 146437
 
3.2%
r 129266
 
2.9%
e 109856
 
2.4%
Other values (91) 1771814
39.2%

habitat
Text

Missing 

Distinct54569
Distinct (%)49.4%
Missing877971
Missing (%)88.8%
Memory size7.5 MiB
2025-01-07T10:43:24.283266image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length567
Median length292
Mean length33.5132979
Min length1

Characters and Unicode

Total characters3700907
Distinct characters129
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43682 ?
Unique (%)39.6%

Sample

1st rowErect.
2nd rowPlanted
3rd rowHillsides covered with broad-leaved forest, understory with Arthrostylidium, Rubus, and numerous ferns, epiphytes and Usnea.
4th rowOpen to closed forest with Pinus contorta, Populus tremuloides, Purshia tridentata, and Ribes cereum.
5th rowDeep secondary forest; clay soil
ValueCountFrequency (%)
forest 28539
 
5.0%
on 19755
 
3.5%
and 16182
 
2.8%
in 14715
 
2.6%
with 11801
 
2.1%
of 10905
 
1.9%
along 6428
 
1.1%
de 6077
 
1.1%
soil 5416
 
1.0%
sand 4830
 
0.8%
Other values (19599) 444497
78.1%
2025-01-07T10:43:24.564337image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
458714
12.4%
e 334644
 
9.0%
a 293407
 
7.9%
o 267034
 
7.2%
r 232740
 
6.3%
s 232487
 
6.3%
n 229134
 
6.2%
i 195932
 
5.3%
t 185312
 
5.0%
l 144970
 
3.9%
Other values (119) 1126533
30.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3700907
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
458714
12.4%
e 334644
 
9.0%
a 293407
 
7.9%
o 267034
 
7.2%
r 232740
 
6.3%
s 232487
 
6.3%
n 229134
 
6.2%
i 195932
 
5.3%
t 185312
 
5.0%
l 144970
 
3.9%
Other values (119) 1126533
30.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3700907
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
458714
12.4%
e 334644
 
9.0%
a 293407
 
7.9%
o 267034
 
7.2%
r 232740
 
6.3%
s 232487
 
6.3%
n 229134
 
6.2%
i 195932
 
5.3%
t 185312
 
5.0%
l 144970
 
3.9%
Other values (119) 1126533
30.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3700907
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
458714
12.4%
e 334644
 
9.0%
a 293407
 
7.9%
o 267034
 
7.2%
r 232740
 
6.3%
s 232487
 
6.3%
n 229134
 
6.2%
i 195932
 
5.3%
t 185312
 
5.0%
l 144970
 
3.9%
Other values (119) 1126533
30.4%

samplingProtocol
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

sampleSizeValue
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

sampleSizeUnit
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

samplingEffort
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

fieldNotes
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

eventRemarks
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

locationID
Text

Missing 

Distinct667
Distinct (%)7.4%
Missing979422
Missing (%)99.1%
Memory size7.5 MiB
2025-01-07T10:43:24.770857image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length35
Median length5
Mean length6.010690423
Min length1

Characters and Unicode

Total characters53976
Distinct characters65
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique202 ?
Unique (%)2.2%

Sample

1st row66-10
2nd row69-11
3rd row64-51
4th row66-14
5th row64-34
ValueCountFrequency (%)
station 1070
 
10.3%
ms04 374
 
3.6%
66-24 305
 
2.9%
61 200
 
1.9%
64-47 131
 
1.3%
64-48 130
 
1.3%
69-14 124
 
1.2%
64-45 98
 
0.9%
66-28 92
 
0.9%
64-06 90
 
0.9%
Other values (654) 7783
74.9%
2025-01-07T10:43:25.040028image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 9454
17.5%
- 7849
14.5%
4 4641
 
8.6%
2 4263
 
7.9%
1 3970
 
7.4%
0 3323
 
6.2%
3 2445
 
4.5%
7 2280
 
4.2%
t 2194
 
4.1%
S 1651
 
3.1%
Other values (55) 11906
22.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 53976
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 9454
17.5%
- 7849
14.5%
4 4641
 
8.6%
2 4263
 
7.9%
1 3970
 
7.4%
0 3323
 
6.2%
3 2445
 
4.5%
7 2280
 
4.2%
t 2194
 
4.1%
S 1651
 
3.1%
Other values (55) 11906
22.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 53976
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 9454
17.5%
- 7849
14.5%
4 4641
 
8.6%
2 4263
 
7.9%
1 3970
 
7.4%
0 3323
 
6.2%
3 2445
 
4.5%
7 2280
 
4.2%
t 2194
 
4.1%
S 1651
 
3.1%
Other values (55) 11906
22.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 53976
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 9454
17.5%
- 7849
14.5%
4 4641
 
8.6%
2 4263
 
7.9%
1 3970
 
7.4%
0 3323
 
6.2%
3 2445
 
4.5%
7 2280
 
4.2%
t 2194
 
4.1%
S 1651
 
3.1%
Other values (55) 11906
22.1%

higherGeographyID
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB
Distinct17498
Distinct (%)1.8%
Missing8448
Missing (%)0.9%
Memory size7.5 MiB
2025-01-07T10:43:25.245883image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length133
Median length113
Mean length40.94448923
Min length5

Characters and Unicode

Total characters40123716
Distinct characters134
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6237 ?
Unique (%)0.6%

Sample

1st rowNorth America, United States, Florida
2nd rowSouth America - Neotropics, Peru, Piura
3rd rowSouth America, Argentina, Formosa
4th rowSouth America - Neotropics, Venezuela, Carabobo
5th rowAfrica, South Africa
ValueCountFrequency (%)
america 664608
 
12.5%
north 382460
 
7.2%
365184
 
6.8%
neotropics 351203
 
6.6%
united 295755
 
5.5%
states 293830
 
5.5%
south 254482
 
4.8%
mexico 71903
 
1.3%
asia-tropical 66600
 
1.2%
brazil 65997
 
1.2%
Other values (10459) 2522178
47.3%
2025-01-07T10:43:25.523677image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4354246
 
10.9%
a 3701367
 
9.2%
i 2990303
 
7.5%
e 2937990
 
7.3%
r 2542856
 
6.3%
t 2505817
 
6.2%
o 2445015
 
6.1%
, 2049394
 
5.1%
n 1579289
 
3.9%
c 1556529
 
3.9%
Other values (124) 13460910
33.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40123716
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4354246
 
10.9%
a 3701367
 
9.2%
i 2990303
 
7.5%
e 2937990
 
7.3%
r 2542856
 
6.3%
t 2505817
 
6.2%
o 2445015
 
6.1%
, 2049394
 
5.1%
n 1579289
 
3.9%
c 1556529
 
3.9%
Other values (124) 13460910
33.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40123716
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4354246
 
10.9%
a 3701367
 
9.2%
i 2990303
 
7.5%
e 2937990
 
7.3%
r 2542856
 
6.3%
t 2505817
 
6.2%
o 2445015
 
6.1%
, 2049394
 
5.1%
n 1579289
 
3.9%
c 1556529
 
3.9%
Other values (124) 13460910
33.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40123716
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4354246
 
10.9%
a 3701367
 
9.2%
i 2990303
 
7.5%
e 2937990
 
7.3%
r 2542856
 
6.3%
t 2505817
 
6.2%
o 2445015
 
6.1%
, 2049394
 
5.1%
n 1579289
 
3.9%
c 1556529
 
3.9%
Other values (124) 13460910
33.5%

continent
Text

Missing 

Distinct7
Distinct (%)< 0.1%
Missing32788
Missing (%)3.3%
Memory size7.5 MiB
2025-01-07T10:43:25.586986image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length13
Mean length11.06743099
Min length4

Characters and Unicode

Total characters10576192
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSOUTH_AMERICA
2nd rowSOUTH_AMERICA
3rd rowSOUTH_AMERICA
4th rowAFRICA
5th rowSOUTH_AMERICA
ValueCountFrequency (%)
north_america 482446
50.5%
south_america 235730
24.7%
asia 113249
 
11.9%
europe 50324
 
5.3%
oceania 37414
 
3.9%
africa 35361
 
3.7%
antarctica 1090
 
0.1%
2025-01-07T10:43:25.693496image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 1811670
17.1%
R 1287397
12.2%
I 905290
8.6%
E 856238
8.1%
O 805914
7.6%
C 793131
7.5%
T 720356
 
6.8%
H 718176
 
6.8%
_ 718176
 
6.8%
M 718176
 
6.8%
Other values (5) 1241668
11.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10576192
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1811670
17.1%
R 1287397
12.2%
I 905290
8.6%
E 856238
8.1%
O 805914
7.6%
C 793131
7.5%
T 720356
 
6.8%
H 718176
 
6.8%
_ 718176
 
6.8%
M 718176
 
6.8%
Other values (5) 1241668
11.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10576192
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1811670
17.1%
R 1287397
12.2%
I 905290
8.6%
E 856238
8.1%
O 805914
7.6%
C 793131
7.5%
T 720356
 
6.8%
H 718176
 
6.8%
_ 718176
 
6.8%
M 718176
 
6.8%
Other values (5) 1241668
11.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10576192
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1811670
17.1%
R 1287397
12.2%
I 905290
8.6%
E 856238
8.1%
O 805914
7.6%
C 793131
7.5%
T 720356
 
6.8%
H 718176
 
6.8%
_ 718176
 
6.8%
M 718176
 
6.8%
Other values (5) 1241668
11.7%

waterBody
Text

Missing 

Distinct75
Distinct (%)1.8%
Missing984227
Missing (%)99.6%
Memory size7.5 MiB
2025-01-07T10:43:25.772493image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length62
Median length61
Mean length25.99209581
Min length8

Characters and Unicode

Total characters108517
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)0.6%

Sample

1st rowNorth Atlantic Ocean, Bay of Fundy
2nd rowNorth Atlantic Ocean, Caribbean Sea
3rd rowNorth Atlantic Ocean, Gulf of Maine, Englishman Bay/Mack Cove
4th rowNorth Atlantic Ocean, Caribbean Sea
5th rowNorth Pacific Ocean
ValueCountFrequency (%)
ocean 3353
20.0%
north 3226
19.2%
atlantic 3034
18.1%
sea 1523
9.1%
caribbean 1284
 
7.6%
of 757
 
4.5%
gulf 720
 
4.3%
maine 576
 
3.4%
bay 526
 
3.1%
pacific 275
 
1.6%
Other values (74) 1519
9.0%
2025-01-07T10:43:25.917898image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 12644
11.7%
12618
11.6%
t 9811
 
9.0%
n 8916
 
8.2%
e 7679
 
7.1%
c 7268
 
6.7%
i 5876
 
5.4%
r 4960
 
4.6%
o 4857
 
4.5%
l 4068
 
3.7%
Other values (42) 29820
27.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 108517
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 12644
11.7%
12618
11.6%
t 9811
 
9.0%
n 8916
 
8.2%
e 7679
 
7.1%
c 7268
 
6.7%
i 5876
 
5.4%
r 4960
 
4.6%
o 4857
 
4.5%
l 4068
 
3.7%
Other values (42) 29820
27.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 108517
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 12644
11.7%
12618
11.6%
t 9811
 
9.0%
n 8916
 
8.2%
e 7679
 
7.1%
c 7268
 
6.7%
i 5876
 
5.4%
r 4960
 
4.6%
o 4857
 
4.5%
l 4068
 
3.7%
Other values (42) 29820
27.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 108517
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 12644
11.7%
12618
11.6%
t 9811
 
9.0%
n 8916
 
8.2%
e 7679
 
7.1%
c 7268
 
6.7%
i 5876
 
5.4%
r 4960
 
4.6%
o 4857
 
4.5%
l 4068
 
3.7%
Other values (42) 29820
27.5%

islandGroup
Text

Missing 

Distinct362
Distinct (%)1.5%
Missing963568
Missing (%)97.5%
Memory size7.5 MiB
2025-01-07T10:43:26.108802image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length42
Median length39
Mean length14.85515825
Min length5

Characters and Unicode

Total characters368913
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85 ?
Unique (%)0.3%

Sample

1st rowGreater Antilles
2nd rowGreater Antilles
3rd rowElizabeth Islands
4th rowChannel Islands
5th rowGreater Antilles
ValueCountFrequency (%)
antilles 7095
 
12.5%
greater 7095
 
12.5%
islands 5085
 
9.0%
is 4355
 
7.7%
group 3620
 
6.4%
new 1627
 
2.9%
guinea 1329
 
2.3%
keys 1172
 
2.1%
channel 1169
 
2.1%
florida 1110
 
2.0%
Other values (325) 23144
40.7%
2025-01-07T10:43:26.374490image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 37198
 
10.1%
a 33600
 
9.1%
31967
 
8.7%
s 29203
 
7.9%
l 28135
 
7.6%
r 26374
 
7.1%
n 24868
 
6.7%
t 19411
 
5.3%
i 18406
 
5.0%
G 13341
 
3.6%
Other values (52) 106410
28.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 368913
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 37198
 
10.1%
a 33600
 
9.1%
31967
 
8.7%
s 29203
 
7.9%
l 28135
 
7.6%
r 26374
 
7.1%
n 24868
 
6.7%
t 19411
 
5.3%
i 18406
 
5.0%
G 13341
 
3.6%
Other values (52) 106410
28.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 368913
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 37198
 
10.1%
a 33600
 
9.1%
31967
 
8.7%
s 29203
 
7.9%
l 28135
 
7.6%
r 26374
 
7.1%
n 24868
 
6.7%
t 19411
 
5.3%
i 18406
 
5.0%
G 13341
 
3.6%
Other values (52) 106410
28.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 368913
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 37198
 
10.1%
a 33600
 
9.1%
31967
 
8.7%
s 29203
 
7.9%
l 28135
 
7.6%
r 26374
 
7.1%
n 24868
 
6.7%
t 19411
 
5.3%
i 18406
 
5.0%
G 13341
 
3.6%
Other values (52) 106410
28.8%

island
Text

Missing 

Distinct2614
Distinct (%)3.2%
Missing906001
Missing (%)91.7%
Memory size7.5 MiB
2025-01-07T10:43:26.575285image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length48
Median length43
Mean length9.546267642
Min length2

Characters and Unicode

Total characters786622
Distinct characters76
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique943 ?
Unique (%)1.1%

Sample

1st rowRota
2nd rowHispaniola
3rd rowNorth Island
4th rowKaua'i
5th rowHispaniola Island
ValueCountFrequency (%)
hispaniola 10778
 
8.5%
island 9771
 
7.7%
cuba 4961
 
3.9%
oahu 3726
 
2.9%
st 2657
 
2.1%
kaua'i 2655
 
2.1%
new 2291
 
1.8%
jamaica 2258
 
1.8%
isla 2167
 
1.7%
luzon 2129
 
1.7%
Other values (2138) 83021
65.7%
2025-01-07T10:43:26.849336image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 125254
15.9%
i 61933
 
7.9%
n 52763
 
6.7%
o 47475
 
6.0%
44013
 
5.6%
l 41457
 
5.3%
u 38087
 
4.8%
e 37522
 
4.8%
s 35293
 
4.5%
r 27973
 
3.6%
Other values (66) 274852
34.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 786622
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 125254
15.9%
i 61933
 
7.9%
n 52763
 
6.7%
o 47475
 
6.0%
44013
 
5.6%
l 41457
 
5.3%
u 38087
 
4.8%
e 37522
 
4.8%
s 35293
 
4.5%
r 27973
 
3.6%
Other values (66) 274852
34.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 786622
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 125254
15.9%
i 61933
 
7.9%
n 52763
 
6.7%
o 47475
 
6.0%
44013
 
5.6%
l 41457
 
5.3%
u 38087
 
4.8%
e 37522
 
4.8%
s 35293
 
4.5%
r 27973
 
3.6%
Other values (66) 274852
34.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 786622
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 125254
15.9%
i 61933
 
7.9%
n 52763
 
6.7%
o 47475
 
6.0%
44013
 
5.6%
l 41457
 
5.3%
u 38087
 
4.8%
e 37522
 
4.8%
s 35293
 
4.5%
r 27973
 
3.6%
Other values (66) 274852
34.9%

countryCode
Text

Missing 

Distinct233
Distinct (%)< 0.1%
Missing10855
Missing (%)1.1%
Memory size7.5 MiB
2025-01-07T10:43:27.026424image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1955094
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowUS
2nd rowPE
3rd rowAR
4th rowVE
5th rowZA
ValueCountFrequency (%)
us 291222
29.8%
br 65995
 
6.8%
mx 63561
 
6.5%
co 36051
 
3.7%
ve 26234
 
2.7%
pe 25485
 
2.6%
ca 24554
 
2.5%
cn 23614
 
2.4%
ec 19520
 
2.0%
ph 18818
 
1.9%
Other values (223) 382493
39.1%
2025-01-07T10:43:27.249491image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 323622
16.6%
S 314243
16.1%
C 149828
 
7.7%
R 124243
 
6.4%
P 103814
 
5.3%
B 95778
 
4.9%
M 95536
 
4.9%
E 89602
 
4.6%
A 79231
 
4.1%
X 63569
 
3.3%
Other values (16) 515628
26.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1955094
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 323622
16.6%
S 314243
16.1%
C 149828
 
7.7%
R 124243
 
6.4%
P 103814
 
5.3%
B 95778
 
4.9%
M 95536
 
4.9%
E 89602
 
4.6%
A 79231
 
4.1%
X 63569
 
3.3%
Other values (16) 515628
26.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1955094
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 323622
16.6%
S 314243
16.1%
C 149828
 
7.7%
R 124243
 
6.4%
P 103814
 
5.3%
B 95778
 
4.9%
M 95536
 
4.9%
E 89602
 
4.6%
A 79231
 
4.1%
X 63569
 
3.3%
Other values (16) 515628
26.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1955094
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 323622
16.6%
S 314243
16.1%
C 149828
 
7.7%
R 124243
 
6.4%
P 103814
 
5.3%
B 95778
 
4.9%
M 95536
 
4.9%
E 89602
 
4.6%
A 79231
 
4.1%
X 63569
 
3.3%
Other values (16) 515628
26.4%

stateProvince
Text

Missing 

Distinct3164
Distinct (%)0.4%
Missing219376
Missing (%)22.2%
Memory size7.5 MiB
2025-01-07T10:43:27.450675image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length52
Median length49
Mean length9.001383568
Min length1

Characters and Unicode

Total characters6922298
Distinct characters119
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique709 ?
Unique (%)0.1%

Sample

1st rowFlorida
2nd rowPiura
3rd rowFormosa
4th rowCarabobo
5th rowManabí
ValueCountFrequency (%)
california 44326
 
4.4%
new 23059
 
2.3%
florida 19421
 
1.9%
virginia 15940
 
1.6%
texas 15589
 
1.5%
alaska 14760
 
1.5%
amazonas 13297
 
1.3%
hawaii 12078
 
1.2%
arizona 11151
 
1.1%
san 11038
 
1.1%
Other values (2927) 831105
82.1%
2025-01-07T10:43:27.730011image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1075650
15.5%
i 567474
 
8.2%
n 508752
 
7.3%
o 506284
 
7.3%
r 439940
 
6.4%
e 348295
 
5.0%
s 278225
 
4.0%
l 274188
 
4.0%
t 243136
 
3.5%
242738
 
3.5%
Other values (109) 2437616
35.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6922298
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1075650
15.5%
i 567474
 
8.2%
n 508752
 
7.3%
o 506284
 
7.3%
r 439940
 
6.4%
e 348295
 
5.0%
s 278225
 
4.0%
l 274188
 
4.0%
t 243136
 
3.5%
242738
 
3.5%
Other values (109) 2437616
35.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6922298
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1075650
15.5%
i 567474
 
8.2%
n 508752
 
7.3%
o 506284
 
7.3%
r 439940
 
6.4%
e 348295
 
5.0%
s 278225
 
4.0%
l 274188
 
4.0%
t 243136
 
3.5%
242738
 
3.5%
Other values (109) 2437616
35.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6922298
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1075650
15.5%
i 567474
 
8.2%
n 508752
 
7.3%
o 506284
 
7.3%
r 439940
 
6.4%
e 348295
 
5.0%
s 278225
 
4.0%
l 274188
 
4.0%
t 243136
 
3.5%
242738
 
3.5%
Other values (109) 2437616
35.2%

county
Text

Missing 

Distinct7486
Distinct (%)4.6%
Missing826754
Missing (%)83.6%
Memory size7.5 MiB
2025-01-07T10:43:27.936913image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length49
Median length44
Mean length9.169770118
Min length1

Characters and Unicode

Total characters1482275
Distinct characters103
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2670 ?
Unique (%)1.7%

Sample

1st rowParroquia
2nd rowDuval
3rd rowBoulder
4th rowCantal
5th rowArlington
ValueCountFrequency (%)
county 12307
 
5.4%
san 7180
 
3.2%
prince 4211
 
1.8%
honolulu 4162
 
1.8%
santa 3941
 
1.7%
los 3095
 
1.4%
angeles 3053
 
1.3%
montgomery 3051
 
1.3%
george's 2971
 
1.3%
maui 2856
 
1.3%
Other values (6200) 181096
79.5%
2025-01-07T10:43:28.293315image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 169425
 
11.4%
o 120482
 
8.1%
n 117091
 
7.9%
e 113146
 
7.6%
r 92576
 
6.2%
i 86084
 
5.8%
t 67805
 
4.6%
u 67402
 
4.5%
66275
 
4.5%
l 63833
 
4.3%
Other values (93) 518156
35.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1482275
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 169425
 
11.4%
o 120482
 
8.1%
n 117091
 
7.9%
e 113146
 
7.6%
r 92576
 
6.2%
i 86084
 
5.8%
t 67805
 
4.6%
u 67402
 
4.5%
66275
 
4.5%
l 63833
 
4.3%
Other values (93) 518156
35.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1482275
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 169425
 
11.4%
o 120482
 
8.1%
n 117091
 
7.9%
e 113146
 
7.6%
r 92576
 
6.2%
i 86084
 
5.8%
t 67805
 
4.6%
u 67402
 
4.5%
66275
 
4.5%
l 63833
 
4.3%
Other values (93) 518156
35.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1482275
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 169425
 
11.4%
o 120482
 
8.1%
n 117091
 
7.9%
e 113146
 
7.6%
r 92576
 
6.2%
i 86084
 
5.8%
t 67805
 
4.6%
u 67402
 
4.5%
66275
 
4.5%
l 63833
 
4.3%
Other values (93) 518156
35.0%

municipality
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

locality
Text

Missing 

Distinct617492
Distinct (%)67.4%
Missing72708
Missing (%)7.4%
Memory size7.5 MiB
2025-01-07T10:43:28.608889image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length373493
Median length322
Mean length47.98270492
Min length1

Characters and Unicode

Total characters43937475
Distinct characters309
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique529251 ?
Unique (%)57.8%

Sample

1st rowGulf of Mexico
2nd rowDept. Piura: Ayabaca
3rd rowDep. Pilcomayo. al E a 2 Km de P. Porteño.
4th rowSelva siempre verde en las quebradas al norte de Los Tanques, arriba de la Planta Eléctrica, en las cabeceras del Río San Gián, al sur de Borburata.
5th rowFlat terrain near Skukuza rest camp, Kruger National Park.
ValueCountFrequency (%)
of 347842
 
5.0%
de 133785
 
1.9%
the 82580
 
1.2%
km 81320
 
1.2%
near 74890
 
1.1%
on 60171
 
0.9%
and 59914
 
0.9%
in 57394
 
0.8%
county 55824
 
0.8%
la 50630
 
0.7%
Other values (260445) 5924543
85.5%
2025-01-07T10:43:28.990708image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5977545
 
13.6%
a 3997394
 
9.1%
e 3081991
 
7.0%
o 2904902
 
6.6%
n 2422306
 
5.5%
i 2249317
 
5.1%
r 2223035
 
5.1%
t 1935539
 
4.4%
l 1565888
 
3.6%
s 1518943
 
3.5%
Other values (299) 16060615
36.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 43937475
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5977545
 
13.6%
a 3997394
 
9.1%
e 3081991
 
7.0%
o 2904902
 
6.6%
n 2422306
 
5.5%
i 2249317
 
5.1%
r 2223035
 
5.1%
t 1935539
 
4.4%
l 1565888
 
3.6%
s 1518943
 
3.5%
Other values (299) 16060615
36.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 43937475
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5977545
 
13.6%
a 3997394
 
9.1%
e 3081991
 
7.0%
o 2904902
 
6.6%
n 2422306
 
5.5%
i 2249317
 
5.1%
r 2223035
 
5.1%
t 1935539
 
4.4%
l 1565888
 
3.6%
s 1518943
 
3.5%
Other values (299) 16060615
36.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 43937475
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5977545
 
13.6%
a 3997394
 
9.1%
e 3081991
 
7.0%
o 2904902
 
6.6%
n 2422306
 
5.5%
i 2249317
 
5.1%
r 2223035
 
5.1%
t 1935539
 
4.4%
l 1565888
 
3.6%
s 1518943
 
3.5%
Other values (299) 16060615
36.6%

verbatimLocality
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

verbatimElevation
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

verticalDatum
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

verbatimDepth
Text

Missing 

Distinct9
Distinct (%)0.2%
Missing983702
Missing (%)99.5%
Memory size7.5 MiB
2025-01-07T10:43:29.054216image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length29
Median length3
Mean length3.033617021
Min length2

Characters and Unicode

Total characters14258
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.1%

Sample

1st rowca.
2nd rowca.
3rd rowca.
4th rowca.
5th rowca.
ValueCountFrequency (%)
ca 4691
99.3%
intertidal 11
 
0.2%
mlw 6
 
0.1%
above 4
 
0.1%
below 2
 
< 0.1%
infralittoral 1
 
< 0.1%
4-8 1
 
< 0.1%
feet 1
 
< 0.1%
mean 1
 
< 0.1%
low 1
 
< 0.1%
Other values (5) 5
 
0.1%
2025-01-07T10:43:29.156307image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 4710
33.0%
c 4691
32.9%
. 4653
32.6%
t 27
 
0.2%
24
 
0.2%
l 23
 
0.2%
e 21
 
0.1%
r 14
 
0.1%
n 13
 
0.1%
i 13
 
0.1%
Other values (15) 69
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14258
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 4710
33.0%
c 4691
32.9%
. 4653
32.6%
t 27
 
0.2%
24
 
0.2%
l 23
 
0.2%
e 21
 
0.1%
r 14
 
0.1%
n 13
 
0.1%
i 13
 
0.1%
Other values (15) 69
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14258
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 4710
33.0%
c 4691
32.9%
. 4653
32.6%
t 27
 
0.2%
24
 
0.2%
l 23
 
0.2%
e 21
 
0.1%
r 14
 
0.1%
n 13
 
0.1%
i 13
 
0.1%
Other values (15) 69
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14258
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 4710
33.0%
c 4691
32.9%
. 4653
32.6%
t 27
 
0.2%
24
 
0.2%
l 23
 
0.2%
e 21
 
0.1%
r 14
 
0.1%
n 13
 
0.1%
i 13
 
0.1%
Other values (15) 69
 
0.5%

minimumDistanceAboveSurfaceInMeters
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

maximumDistanceAboveSurfaceInMeters
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

locationAccordingTo
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

locationRemarks
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

decimalLatitude
Real number (ℝ)

Missing 

Distinct30964
Distinct (%)21.0%
Missing841005
Missing (%)85.1%
Infinite0
Infinite (%)0.0%
Mean10.99996396
Minimum-84.58
Maximum89.65
Zeros66
Zeros (%)< 0.1%
Negative41700
Negative (%)4.2%
Memory size7.5 MiB
2025-01-07T10:43:29.217522image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-84.58
5-th percentile-25.25
Q1-1.75
median6.2
Q326.67
95-th percentile51.0517
Maximum89.65
Range174.23
Interquartile range (IQR)28.42

Descriptive statistics

Standard deviation23.22681712
Coefficient of variation (CV)2.111535748
Kurtosis0.1792545485
Mean10.99996396
Median Absolute Deviation (MAD)13.8983
Skewness0.2047602199
Sum1621361.688
Variance539.4850337
MonotonicityNot monotonic
2025-01-07T10:43:29.276897image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.9694 858
 
0.1%
38.895 856
 
0.1%
9.405 393
 
< 0.1%
3.61 370
 
< 0.1%
5.16667 340
 
< 0.1%
5.2 332
 
< 0.1%
0.35 331
 
< 0.1%
38.8664 324
 
< 0.1%
0.83 312
 
< 0.1%
-12.83 309
 
< 0.1%
Other values (30954) 142972
 
14.5%
(Missing) 841005
85.1%
ValueCountFrequency (%)
-84.58 1
 
< 0.1%
-78.2917 1
 
< 0.1%
-78.12 1
 
< 0.1%
-78.05 3
< 0.1%
-77.9 3
< 0.1%
ValueCountFrequency (%)
89.65 3
< 0.1%
89.4074 1
 
< 0.1%
89.208 3
< 0.1%
86.6028 1
 
< 0.1%
85.475 1
 
< 0.1%

decimalLongitude
Real number (ℝ)

Missing 

Distinct32805
Distinct (%)22.3%
Missing841005
Missing (%)85.1%
Infinite0
Infinite (%)0.0%
Mean-52.18833747
Minimum-179.92
Maximum179.967
Zeros30
Zeros (%)< 0.1%
Negative125223
Negative (%)12.7%
Memory size7.5 MiB
2025-01-07T10:43:29.335389image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-179.92
5-th percentile-121.689
Q1-78.9978
median-67.7177
Q3-54.75
95-th percentile120
Maximum179.967
Range359.887
Interquartile range (IQR)24.2478

Descriptive statistics

Standard deviation65.6338469
Coefficient of variation (CV)-1.25763437
Kurtosis2.775615301
Mean-52.18833747
Median Absolute Deviation (MAD)11.999
Skewness1.738833902
Sum-7692404.379
Variance4307.801859
MonotonicityNot monotonic
2025-01-07T10:43:29.401089image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-77.1767 842
 
0.1%
-77.0367 831
 
0.1%
-59.4833 487
 
< 0.1%
-79.8635 382
 
< 0.1%
-53.2 382
 
< 0.1%
-52.33 360
 
< 0.1%
-59.48 325
 
< 0.1%
-79.73 307
 
< 0.1%
-88.08 302
 
< 0.1%
-70.95 301
 
< 0.1%
Other values (32795) 142878
 
14.5%
(Missing) 841005
85.1%
ValueCountFrequency (%)
-179.92 1
 
< 0.1%
-179.17 1
 
< 0.1%
-179.133 16
< 0.1%
-178.92 1
 
< 0.1%
-178.838 1
 
< 0.1%
ValueCountFrequency (%)
179.967 1
< 0.1%
179.9 1
< 0.1%
179.516 1
< 0.1%
179.383 2
< 0.1%
179.133 1
< 0.1%

coordinateUncertaintyInMeters
Real number (ℝ)

Missing 

Distinct20
Distinct (%)1.4%
Missing987002
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean4467.857143
Minimum50
Maximum16000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:29.455089image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile250
Q1500
median1000
Q35000
95-th percentile16000
Maximum16000
Range15950
Interquartile range (IQR)4500

Descriptive statistics

Standard deviation6080.716222
Coefficient of variation (CV)1.36099164
Kurtosis-0.1853501471
Mean4467.857143
Median Absolute Deviation (MAD)750
Skewness1.288656968
Sum6255000
Variance36975109.77
MonotonicityNot monotonic
2025-01-07T10:43:29.507971image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
16000 286
 
< 0.1%
1000 277
 
< 0.1%
500 234
 
< 0.1%
250 145
 
< 0.1%
3000 135
 
< 0.1%
5000 68
 
< 0.1%
750 67
 
< 0.1%
1500 51
 
< 0.1%
2000 38
 
< 0.1%
3500 32
 
< 0.1%
Other values (10) 67
 
< 0.1%
(Missing) 987002
99.9%
ValueCountFrequency (%)
50 1
 
< 0.1%
100 21
 
< 0.1%
150 1
 
< 0.1%
200 10
 
< 0.1%
250 145
< 0.1%
ValueCountFrequency (%)
16000 286
< 0.1%
15000 11
 
< 0.1%
5000 68
 
< 0.1%
3500 32
 
< 0.1%
3000 135
< 0.1%

coordinatePrecision
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

pointRadiusSpatialFit
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB
Distinct4
Distinct (%)0.1%
Missing980404
Missing (%)99.2%
Memory size7.5 MiB
2025-01-07T10:43:29.552029image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length23
Median length23
Mean length22.98012003
Min length4

Characters and Unicode

Total characters183795
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowDegrees Minutes Seconds
2nd rowDegrees Minutes Seconds
3rd rowDegrees Minutes Seconds
4th rowDegrees Minutes Seconds
5th rowDegrees Minutes Seconds
ValueCountFrequency (%)
degrees 7992
33.3%
minutes 7986
33.3%
seconds 7986
33.3%
decimal 6
 
< 0.1%
quad 5
 
< 0.1%
unknown 1
 
< 0.1%
2025-01-07T10:43:29.660338image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 39954
21.7%
s 23964
13.0%
15978
 
8.7%
n 15975
 
8.7%
D 7997
 
4.4%
r 7992
 
4.3%
g 7992
 
4.3%
d 7992
 
4.3%
i 7992
 
4.3%
c 7992
 
4.3%
Other values (13) 39967
21.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 183795
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 39954
21.7%
s 23964
13.0%
15978
 
8.7%
n 15975
 
8.7%
D 7997
 
4.4%
r 7992
 
4.3%
g 7992
 
4.3%
d 7992
 
4.3%
i 7992
 
4.3%
c 7992
 
4.3%
Other values (13) 39967
21.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 183795
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 39954
21.7%
s 23964
13.0%
15978
 
8.7%
n 15975
 
8.7%
D 7997
 
4.4%
r 7992
 
4.3%
g 7992
 
4.3%
d 7992
 
4.3%
i 7992
 
4.3%
c 7992
 
4.3%
Other values (13) 39967
21.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 183795
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 39954
21.7%
s 23964
13.0%
15978
 
8.7%
n 15975
 
8.7%
D 7997
 
4.4%
r 7992
 
4.3%
g 7992
 
4.3%
d 7992
 
4.3%
i 7992
 
4.3%
c 7992
 
4.3%
Other values (13) 39967
21.7%

verbatimSRS
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Memory size7.5 MiB
2025-01-07T10:43:29.706962image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters10
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1938-11-11
ValueCountFrequency (%)
1938-11-11 1
100.0%
2025-01-07T10:43:29.804473image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 5
50.0%
- 2
 
20.0%
9 1
 
10.0%
3 1
 
10.0%
8 1
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 5
50.0%
- 2
 
20.0%
9 1
 
10.0%
3 1
 
10.0%
8 1
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 5
50.0%
- 2
 
20.0%
9 1
 
10.0%
3 1
 
10.0%
8 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 5
50.0%
- 2
 
20.0%
9 1
 
10.0%
3 1
 
10.0%
8 1
 
10.0%

footprintWKT
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

footprintSRS
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean315
Minimum315
Maximum315
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:29.860544image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum315
5-th percentile315
Q1315
median315
Q3315
95-th percentile315
Maximum315
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Mean315
Median Absolute Deviation (MAD)0
Skewnessnan
Sum315
Variancenan
MonotonicityStrictly increasing
2025-01-07T10:43:29.903909image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
315 1
 
< 0.1%
(Missing) 988401
> 99.9%
ValueCountFrequency (%)
315 1
< 0.1%
ValueCountFrequency (%)
315 1
< 0.1%

footprintSpatialFit
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean315
Minimum315
Maximum315
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:29.944909image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum315
5-th percentile315
Q1315
median315
Q3315
95-th percentile315
Maximum315
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Mean315
Median Absolute Deviation (MAD)0
Skewnessnan
Sum315
Variancenan
MonotonicityStrictly increasing
2025-01-07T10:43:29.986889image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
315 1
 
< 0.1%
(Missing) 988401
> 99.9%
ValueCountFrequency (%)
315 1
< 0.1%
ValueCountFrequency (%)
315 1
< 0.1%

georeferencedBy
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean1938
Minimum1938
Maximum1938
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:30.028237image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1938
5-th percentile1938
Q11938
median1938
Q31938
95-th percentile1938
Maximum1938
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Mean1938
Median Absolute Deviation (MAD)0
Skewnessnan
Sum1938
Variancenan
MonotonicityStrictly increasing
2025-01-07T10:43:30.072236image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
1938 1
 
< 0.1%
(Missing) 988401
> 99.9%
ValueCountFrequency (%)
1938 1
< 0.1%
ValueCountFrequency (%)
1938 1
< 0.1%

georeferencedDate
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean11
Minimum11
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:30.115770image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q111
median11
Q311
95-th percentile11
Maximum11
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Mean11
Median Absolute Deviation (MAD)0
Skewnessnan
Sum11
Variancenan
MonotonicityStrictly increasing
2025-01-07T10:43:30.162442image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
11 1
 
< 0.1%
(Missing) 988401
> 99.9%
ValueCountFrequency (%)
11 1
< 0.1%
ValueCountFrequency (%)
11 1
< 0.1%

georeferenceProtocol
Text

Missing 

Distinct20
Distinct (%)0.1%
Missing960543
Missing (%)97.2%
Memory size7.5 MiB
2025-01-07T10:43:30.207120image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length17
Median length16
Mean length8.355289135
Min length2

Characters and Unicode

Total characters232770
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowGazetteer
2nd rowGazetteer
3rd rowGazetteer
4th rowGazetteer
5th rowLabel
ValueCountFrequency (%)
gazetteer 10962
30.3%
gps 5054
14.0%
gis 4557
12.6%
arcview 4557
12.6%
label 3720
 
10.3%
google 3348
 
9.3%
maps 2711
 
7.5%
earth 637
 
1.8%
source 400
 
1.1%
g-1 76
 
0.2%
Other values (11) 162
 
0.4%
2025-01-07T10:43:30.325375image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 44988
19.3%
G 23987
 
10.3%
t 22571
 
9.7%
a 18101
 
7.8%
r 16566
 
7.1%
z 10962
 
4.7%
S 9997
 
4.3%
8325
 
3.6%
o 7139
 
3.1%
l 7094
 
3.0%
Other values (30) 63040
27.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 232770
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 44988
19.3%
G 23987
 
10.3%
t 22571
 
9.7%
a 18101
 
7.8%
r 16566
 
7.1%
z 10962
 
4.7%
S 9997
 
4.3%
8325
 
3.6%
o 7139
 
3.1%
l 7094
 
3.0%
Other values (30) 63040
27.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 232770
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 44988
19.3%
G 23987
 
10.3%
t 22571
 
9.7%
a 18101
 
7.8%
r 16566
 
7.1%
z 10962
 
4.7%
S 9997
 
4.3%
8325
 
3.6%
o 7139
 
3.1%
l 7094
 
3.0%
Other values (30) 63040
27.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 232770
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 44988
19.3%
G 23987
 
10.3%
t 22571
 
9.7%
a 18101
 
7.8%
r 16566
 
7.1%
z 10962
 
4.7%
S 9997
 
4.3%
8325
 
3.6%
o 7139
 
3.1%
l 7094
 
3.0%
Other values (30) 63040
27.1%

georeferenceSources
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Memory size7.5 MiB
2025-01-07T10:43:30.369376image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters11
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row11 Nov 1938
ValueCountFrequency (%)
11 1
33.3%
nov 1
33.3%
1938 1
33.3%
2025-01-07T10:43:30.462513image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 3
27.3%
2
18.2%
N 1
 
9.1%
o 1
 
9.1%
v 1
 
9.1%
9 1
 
9.1%
3 1
 
9.1%
8 1
 
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 3
27.3%
2
18.2%
N 1
 
9.1%
o 1
 
9.1%
v 1
 
9.1%
9 1
 
9.1%
3 1
 
9.1%
8 1
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 3
27.3%
2
18.2%
N 1
 
9.1%
o 1
 
9.1%
v 1
 
9.1%
9 1
 
9.1%
3 1
 
9.1%
8 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 3
27.3%
2
18.2%
N 1
 
9.1%
o 1
 
9.1%
v 1
 
9.1%
9 1
 
9.1%
3 1
 
9.1%
8 1
 
9.1%

georeferenceRemarks
Text

Missing 

Distinct38
Distinct (%)33.6%
Missing988289
Missing (%)> 99.9%
Memory size7.5 MiB
2025-01-07T10:43:30.558642image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length53
Median length40
Mean length18.7699115
Min length3

Characters and Unicode

Total characters2121
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)18.6%

Sample

1st row+-1000m
2nd rowstop 1 - beginning of bike path, along GW pkwy
3rd rowca.; ca.
4th rowstop 1-ditch; stop 2- polkweed; stop 3; stop 4
5th rowLong. 4 8 W - 4 15 W
ValueCountFrequency (%)
stop 48
 
10.5%
4 29
 
6.3%
26
 
5.7%
ca 23
 
5.0%
w 22
 
4.8%
1 21
 
4.6%
as 13
 
2.8%
seconds 13
 
2.8%
of 13
 
2.8%
invalid 13
 
2.8%
Other values (63) 238
51.9%
2025-01-07T10:43:30.718861image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
346
16.3%
o 142
 
6.7%
n 124
 
5.8%
a 118
 
5.6%
t 118
 
5.6%
e 116
 
5.5%
i 113
 
5.3%
s 94
 
4.4%
p 82
 
3.9%
l 81
 
3.8%
Other values (44) 787
37.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2121
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
346
16.3%
o 142
 
6.7%
n 124
 
5.8%
a 118
 
5.6%
t 118
 
5.6%
e 116
 
5.5%
i 113
 
5.3%
s 94
 
4.4%
p 82
 
3.9%
l 81
 
3.8%
Other values (44) 787
37.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2121
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
346
16.3%
o 142
 
6.7%
n 124
 
5.8%
a 118
 
5.6%
t 118
 
5.6%
e 116
 
5.5%
i 113
 
5.3%
s 94
 
4.4%
p 82
 
3.9%
l 81
 
3.8%
Other values (44) 787
37.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2121
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
346
16.3%
o 142
 
6.7%
n 124
 
5.8%
a 118
 
5.6%
t 118
 
5.6%
e 116
 
5.5%
i 113
 
5.3%
s 94
 
4.4%
p 82
 
3.9%
l 81
 
3.8%
Other values (44) 787
37.1%

geologicalContextID
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

earliestEonOrLowestEonothem
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

latestEonOrHighestEonothem
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

earliestEraOrLowestErathem
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

latestEraOrHighestErathem
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

earliestPeriodOrLowestSystem
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

latestPeriodOrHighestSystem
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

earliestEpochOrLowestSeries
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

latestEpochOrHighestSeries
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Memory size7.5 MiB
2025-01-07T10:43:30.778391image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length42
Median length42
Mean length42
Min length42

Characters and Unicode

Total characters42
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowSouth America - Neotropics, Colombia, Meta
ValueCountFrequency (%)
south 1
16.7%
america 1
16.7%
1
16.7%
neotropics 1
16.7%
colombia 1
16.7%
meta 1
16.7%
2025-01-07T10:43:30.878900image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 5
11.9%
5
11.9%
t 3
 
7.1%
a 3
 
7.1%
i 3
 
7.1%
e 3
 
7.1%
c 2
 
4.8%
r 2
 
4.8%
, 2
 
4.8%
m 2
 
4.8%
Other values (12) 12
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 42
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 5
11.9%
5
11.9%
t 3
 
7.1%
a 3
 
7.1%
i 3
 
7.1%
e 3
 
7.1%
c 2
 
4.8%
r 2
 
4.8%
, 2
 
4.8%
m 2
 
4.8%
Other values (12) 12
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 42
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 5
11.9%
5
11.9%
t 3
 
7.1%
a 3
 
7.1%
i 3
 
7.1%
e 3
 
7.1%
c 2
 
4.8%
r 2
 
4.8%
, 2
 
4.8%
m 2
 
4.8%
Other values (12) 12
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 42
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 5
11.9%
5
11.9%
t 3
 
7.1%
a 3
 
7.1%
i 3
 
7.1%
e 3
 
7.1%
c 2
 
4.8%
r 2
 
4.8%
, 2
 
4.8%
m 2
 
4.8%
Other values (12) 12
28.6%

earliestAgeOrLowestStage
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Memory size7.5 MiB
2025-01-07T10:43:31.018692image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters13
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowSOUTH_AMERICA
ValueCountFrequency (%)
south_america 1
100.0%
2025-01-07T10:43:31.113125image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 2
15.4%
S 1
7.7%
U 1
7.7%
O 1
7.7%
T 1
7.7%
H 1
7.7%
_ 1
7.7%
M 1
7.7%
E 1
7.7%
R 1
7.7%
Other values (2) 2
15.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 2
15.4%
S 1
7.7%
U 1
7.7%
O 1
7.7%
T 1
7.7%
H 1
7.7%
_ 1
7.7%
M 1
7.7%
E 1
7.7%
R 1
7.7%
Other values (2) 2
15.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 2
15.4%
S 1
7.7%
U 1
7.7%
O 1
7.7%
T 1
7.7%
H 1
7.7%
_ 1
7.7%
M 1
7.7%
E 1
7.7%
R 1
7.7%
Other values (2) 2
15.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 2
15.4%
S 1
7.7%
U 1
7.7%
O 1
7.7%
T 1
7.7%
H 1
7.7%
_ 1
7.7%
M 1
7.7%
E 1
7.7%
R 1
7.7%
Other values (2) 2
15.4%

latestAgeOrHighestStage
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

lowestBiostratigraphicZone
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean7296210
Minimum7296210
Maximum7296210
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:31.165125image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum7296210
5-th percentile7296210
Q17296210
median7296210
Q37296210
95-th percentile7296210
Maximum7296210
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Mean7296210
Median Absolute Deviation (MAD)0
Skewnessnan
Sum7296210
Variancenan
MonotonicityStrictly increasing
2025-01-07T10:43:31.209980image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
7296210 1
 
< 0.1%
(Missing) 988401
> 99.9%
ValueCountFrequency (%)
7296210 1
< 0.1%
ValueCountFrequency (%)
7296210 1
< 0.1%

highestBiostratigraphicZone
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

lithostratigraphicTerms
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Memory size7.5 MiB
2025-01-07T10:43:31.238981image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowCO
ValueCountFrequency (%)
co 1
100.0%
2025-01-07T10:43:31.330939image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 1
50.0%
O 1
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 1
50.0%
O 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 1
50.0%
O 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 1
50.0%
O 1
50.0%

group
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Memory size7.5 MiB
2025-01-07T10:43:31.371938image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowMeta
ValueCountFrequency (%)
meta 1
100.0%
2025-01-07T10:43:31.461872image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
M 1
25.0%
e 1
25.0%
t 1
25.0%
a 1
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 1
25.0%
e 1
25.0%
t 1
25.0%
a 1
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 1
25.0%
e 1
25.0%
t 1
25.0%
a 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 1
25.0%
e 1
25.0%
t 1
25.0%
a 1
25.0%

formation
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

member
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

bed
Text

Missing 

Distinct2
Distinct (%)100.0%
Missing988400
Missing (%)> 99.9%
Memory size7.5 MiB
2025-01-07T10:43:31.507305image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length32
Median length23.5
Mean length23.5
Min length15

Characters and Unicode

Total characters47
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowRinorea pubiflora var. pubiflora
2nd rowVilla Vicencia.
ValueCountFrequency (%)
pubiflora 2
33.3%
rinorea 1
16.7%
var 1
16.7%
villa 1
16.7%
vicencia 1
16.7%
2025-01-07T10:43:31.612289image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 6
12.8%
a 6
12.8%
l 4
 
8.5%
4
 
8.5%
r 4
 
8.5%
o 3
 
6.4%
p 2
 
4.3%
e 2
 
4.3%
n 2
 
4.3%
. 2
 
4.3%
Other values (7) 12
25.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 6
12.8%
a 6
12.8%
l 4
 
8.5%
4
 
8.5%
r 4
 
8.5%
o 3
 
6.4%
p 2
 
4.3%
e 2
 
4.3%
n 2
 
4.3%
. 2
 
4.3%
Other values (7) 12
25.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 6
12.8%
a 6
12.8%
l 4
 
8.5%
4
 
8.5%
r 4
 
8.5%
o 3
 
6.4%
p 2
 
4.3%
e 2
 
4.3%
n 2
 
4.3%
. 2
 
4.3%
Other values (7) 12
25.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 6
12.8%
a 6
12.8%
l 4
 
8.5%
4
 
8.5%
r 4
 
8.5%
o 3
 
6.4%
p 2
 
4.3%
e 2
 
4.3%
n 2
 
4.3%
. 2
 
4.3%
Other values (7) 12
25.5%

identificationID
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

verbatimIdentification
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB
Distinct16
Distinct (%)0.7%
Missing985985
Missing (%)99.8%
Memory size7.5 MiB
2025-01-07T10:43:31.660289image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length12
Median length3
Mean length4.395531651
Min length2

Characters and Unicode

Total characters10624
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st rowcf.
2nd rowcf.
3rd rowcf.
4th rowvel aff.
5th rowvel aff.
ValueCountFrequency (%)
cf 1295
51.4%
aff 610
24.2%
uncertain 368
 
14.6%
s.l 125
 
5.0%
vel 77
 
3.1%
sp 15
 
0.6%
near 13
 
0.5%
nov 13
 
0.5%
s.s 5
 
0.2%
2025-01-07T10:43:31.763128image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
f 2515
23.7%
. 2176
20.5%
c 1663
15.7%
a 991
 
9.3%
n 762
 
7.2%
e 458
 
4.3%
r 381
 
3.6%
t 368
 
3.5%
i 368
 
3.5%
u 365
 
3.4%
Other values (7) 577
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10624
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
f 2515
23.7%
. 2176
20.5%
c 1663
15.7%
a 991
 
9.3%
n 762
 
7.2%
e 458
 
4.3%
r 381
 
3.6%
t 368
 
3.5%
i 368
 
3.5%
u 365
 
3.4%
Other values (7) 577
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10624
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
f 2515
23.7%
. 2176
20.5%
c 1663
15.7%
a 991
 
9.3%
n 762
 
7.2%
e 458
 
4.3%
r 381
 
3.6%
t 368
 
3.5%
i 368
 
3.5%
u 365
 
3.4%
Other values (7) 577
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10624
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
f 2515
23.7%
. 2176
20.5%
c 1663
15.7%
a 991
 
9.3%
n 762
 
7.2%
e 458
 
4.3%
r 381
 
3.6%
t 368
 
3.5%
i 368
 
3.5%
u 365
 
3.4%
Other values (7) 577
 
5.4%

typeStatus
Text

Missing 

Distinct13
Distinct (%)0.1%
Missing967033
Missing (%)97.8%
Memory size7.5 MiB
2025-01-07T10:43:31.817805image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length16
Median length7
Mean length7.474893537
Min length4

Characters and Unicode

Total characters159731
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowISOTYPE
2nd rowISOTYPE
3rd rowHOLOTYPE
4th rowISOTYPE
5th rowISOTYPE
ValueCountFrequency (%)
isotype 13211
61.8%
holotype 4263
 
19.9%
isosyntype 1377
 
6.4%
syntype 1202
 
5.6%
type 444
 
2.1%
isolectotype 434
 
2.0%
lectotype 195
 
0.9%
isoneotype 97
 
0.5%
paratype 75
 
0.4%
neotype 46
 
0.2%
Other values (3) 25
 
0.1%
2025-01-07T10:43:31.929457image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
O 24437
15.3%
Y 23932
15.0%
E 22146
13.9%
T 21998
13.8%
P 21437
13.4%
S 17702
11.1%
I 15176
9.5%
L 4924
 
3.1%
H 4263
 
2.7%
N 2738
 
1.7%
Other values (5) 978
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 159731
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 24437
15.3%
Y 23932
15.0%
E 22146
13.9%
T 21998
13.8%
P 21437
13.4%
S 17702
11.1%
I 15176
9.5%
L 4924
 
3.1%
H 4263
 
2.7%
N 2738
 
1.7%
Other values (5) 978
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 159731
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 24437
15.3%
Y 23932
15.0%
E 22146
13.9%
T 21998
13.8%
P 21437
13.4%
S 17702
11.1%
I 15176
9.5%
L 4924
 
3.1%
H 4263
 
2.7%
N 2738
 
1.7%
Other values (5) 978
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 159731
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 24437
15.3%
Y 23932
15.0%
E 22146
13.9%
T 21998
13.8%
P 21437
13.4%
S 17702
11.1%
I 15176
9.5%
L 4924
 
3.1%
H 4263
 
2.7%
N 2738
 
1.7%
Other values (5) 978
 
0.6%

identifiedBy
Text

Missing 

Distinct4879
Distinct (%)4.0%
Missing866335
Missing (%)87.7%
Memory size7.5 MiB
2025-01-07T10:43:32.132569image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length131
Median length108
Mean length37.6902275
Min length3

Characters and Unicode

Total characters4600733
Distinct characters91
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1781 ?
Unique (%)1.5%

Sample

1st rowBlair, S. M.
2nd rowAcevedo-Rodríguez, P., (BOT), Smithsonian Institution - National Museum of Natural History (UNITED STATES)
3rd rowAcevedo-Rodríguez, P., (BOT), Smithsonian Institution - National Museum of Natural History (UNITED STATES)
4th rowWagner, W. L., (BOT), Smithsonian Institution - National Museum of Natural History (UNITED STATES)
5th rowWagner, W. L., (BOT), Smithsonian Institution - National Museum of Natural History (UNITED STATES)
ValueCountFrequency (%)
united 29831
 
4.2%
states 29821
 
4.2%
of 27693
 
3.9%
27094
 
3.8%
national 26421
 
3.7%
museum 26206
 
3.6%
smithsonian 26080
 
3.6%
natural 26024
 
3.6%
history 26005
 
3.6%
institution 26002
 
3.6%
Other values (4341) 447270
62.3%
2025-01-07T10:43:32.415210image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
596380
 
13.0%
t 259398
 
5.6%
a 250409
 
5.4%
o 245191
 
5.3%
i 229198
 
5.0%
n 225319
 
4.9%
, 198332
 
4.3%
. 187693
 
4.1%
r 186814
 
4.1%
e 183391
 
4.0%
Other values (81) 2038608
44.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4600733
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
596380
 
13.0%
t 259398
 
5.6%
a 250409
 
5.4%
o 245191
 
5.3%
i 229198
 
5.0%
n 225319
 
4.9%
, 198332
 
4.3%
. 187693
 
4.1%
r 186814
 
4.1%
e 183391
 
4.0%
Other values (81) 2038608
44.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4600733
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
596380
 
13.0%
t 259398
 
5.6%
a 250409
 
5.4%
o 245191
 
5.3%
i 229198
 
5.0%
n 225319
 
4.9%
, 198332
 
4.3%
. 187693
 
4.1%
r 186814
 
4.1%
e 183391
 
4.0%
Other values (81) 2038608
44.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4600733
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
596380
 
13.0%
t 259398
 
5.6%
a 250409
 
5.4%
o 245191
 
5.3%
i 229198
 
5.0%
n 225319
 
4.9%
, 198332
 
4.3%
. 187693
 
4.1%
r 186814
 
4.1%
e 183391
 
4.0%
Other values (81) 2038608
44.3%

identifiedByID
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

dateIdentified
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Memory size7.5 MiB
2025-01-07T10:43:32.481208image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length59
Median length59
Mean length59
Min length59

Characters and Unicode

Total characters59
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowPlantae, Dicotyledonae, Malpighiales, Violaceae, Violoideae
ValueCountFrequency (%)
plantae 1
20.0%
dicotyledonae 1
20.0%
malpighiales 1
20.0%
violaceae 1
20.0%
violoideae 1
20.0%
2025-01-07T10:43:32.587738image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 8
13.6%
e 8
13.6%
i 6
10.2%
l 6
10.2%
o 5
8.5%
, 4
 
6.8%
4
 
6.8%
n 2
 
3.4%
V 2
 
3.4%
t 2
 
3.4%
Other values (10) 12
20.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 59
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 8
13.6%
e 8
13.6%
i 6
10.2%
l 6
10.2%
o 5
8.5%
, 4
 
6.8%
4
 
6.8%
n 2
 
3.4%
V 2
 
3.4%
t 2
 
3.4%
Other values (10) 12
20.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 59
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 8
13.6%
e 8
13.6%
i 6
10.2%
l 6
10.2%
o 5
8.5%
, 4
 
6.8%
4
 
6.8%
n 2
 
3.4%
V 2
 
3.4%
t 2
 
3.4%
Other values (10) 12
20.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 59
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 8
13.6%
e 8
13.6%
i 6
10.2%
l 6
10.2%
o 5
8.5%
, 4
 
6.8%
4
 
6.8%
n 2
 
3.4%
V 2
 
3.4%
t 2
 
3.4%
Other values (10) 12
20.3%

identificationReferences
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Memory size7.5 MiB
2025-01-07T10:43:32.632242image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters7
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowPlantae
ValueCountFrequency (%)
plantae 1
100.0%
2025-01-07T10:43:32.723733image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2
28.6%
P 1
14.3%
l 1
14.3%
n 1
14.3%
t 1
14.3%
e 1
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2
28.6%
P 1
14.3%
l 1
14.3%
n 1
14.3%
t 1
14.3%
e 1
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2
28.6%
P 1
14.3%
l 1
14.3%
n 1
14.3%
t 1
14.3%
e 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2
28.6%
P 1
14.3%
l 1
14.3%
n 1
14.3%
t 1
14.3%
e 1
14.3%

identificationVerificationStatus
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Memory size7.5 MiB
2025-01-07T10:43:32.769734image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters12
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowTracheophyta
ValueCountFrequency (%)
tracheophyta 1
100.0%
2025-01-07T10:43:32.863884image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
h 2
16.7%
a 2
16.7%
r 1
8.3%
T 1
8.3%
c 1
8.3%
e 1
8.3%
o 1
8.3%
p 1
8.3%
y 1
8.3%
t 1
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
h 2
16.7%
a 2
16.7%
r 1
8.3%
T 1
8.3%
c 1
8.3%
e 1
8.3%
o 1
8.3%
p 1
8.3%
y 1
8.3%
t 1
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
h 2
16.7%
a 2
16.7%
r 1
8.3%
T 1
8.3%
c 1
8.3%
e 1
8.3%
o 1
8.3%
p 1
8.3%
y 1
8.3%
t 1
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
h 2
16.7%
a 2
16.7%
r 1
8.3%
T 1
8.3%
c 1
8.3%
e 1
8.3%
o 1
8.3%
p 1
8.3%
y 1
8.3%
t 1
8.3%

identificationRemarks
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Memory size7.5 MiB
2025-01-07T10:43:32.909372image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters13
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowMagnoliopsida
ValueCountFrequency (%)
magnoliopsida 1
100.0%
2025-01-07T10:43:33.002690image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2
15.4%
o 2
15.4%
i 2
15.4%
M 1
7.7%
n 1
7.7%
g 1
7.7%
l 1
7.7%
p 1
7.7%
s 1
7.7%
d 1
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2
15.4%
o 2
15.4%
i 2
15.4%
M 1
7.7%
n 1
7.7%
g 1
7.7%
l 1
7.7%
p 1
7.7%
s 1
7.7%
d 1
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2
15.4%
o 2
15.4%
i 2
15.4%
M 1
7.7%
n 1
7.7%
g 1
7.7%
l 1
7.7%
p 1
7.7%
s 1
7.7%
d 1
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2
15.4%
o 2
15.4%
i 2
15.4%
M 1
7.7%
n 1
7.7%
g 1
7.7%
l 1
7.7%
p 1
7.7%
s 1
7.7%
d 1
7.7%

taxonID
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Memory size7.5 MiB
2025-01-07T10:43:33.046690image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters12
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowMalpighiales
ValueCountFrequency (%)
malpighiales 1
100.0%
2025-01-07T10:43:33.144876image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2
16.7%
i 2
16.7%
l 2
16.7%
M 1
8.3%
p 1
8.3%
g 1
8.3%
h 1
8.3%
e 1
8.3%
s 1
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2
16.7%
i 2
16.7%
l 2
16.7%
M 1
8.3%
p 1
8.3%
g 1
8.3%
h 1
8.3%
e 1
8.3%
s 1
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2
16.7%
i 2
16.7%
l 2
16.7%
M 1
8.3%
p 1
8.3%
g 1
8.3%
h 1
8.3%
e 1
8.3%
s 1
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2
16.7%
i 2
16.7%
l 2
16.7%
M 1
8.3%
p 1
8.3%
g 1
8.3%
h 1
8.3%
e 1
8.3%
s 1
8.3%

scientificNameID
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

acceptedNameUsageID
Unsupported

Rejected  Unsupported 

Missing3368
Missing (%)0.3%
Memory size7.5 MiB

parentNameUsageID
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

originalNameUsageID
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

nameAccordingToID
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

namePublishedInID
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Memory size7.5 MiB
2025-01-07T10:43:33.190819image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters7
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowRinorea
ValueCountFrequency (%)
rinorea 1
100.0%
2025-01-07T10:43:33.288697image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 1
14.3%
i 1
14.3%
n 1
14.3%
o 1
14.3%
r 1
14.3%
e 1
14.3%
a 1
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 1
14.3%
i 1
14.3%
n 1
14.3%
o 1
14.3%
r 1
14.3%
e 1
14.3%
a 1
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 1
14.3%
i 1
14.3%
n 1
14.3%
o 1
14.3%
r 1
14.3%
e 1
14.3%
a 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 1
14.3%
i 1
14.3%
n 1
14.3%
o 1
14.3%
r 1
14.3%
e 1
14.3%
a 1
14.3%

taxonConceptID
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Memory size7.5 MiB
2025-01-07T10:43:33.329200image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters7
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowRinorea
ValueCountFrequency (%)
rinorea 1
100.0%
2025-01-07T10:43:33.417991image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 1
14.3%
i 1
14.3%
n 1
14.3%
o 1
14.3%
r 1
14.3%
e 1
14.3%
a 1
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 1
14.3%
i 1
14.3%
n 1
14.3%
o 1
14.3%
r 1
14.3%
e 1
14.3%
a 1
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 1
14.3%
i 1
14.3%
n 1
14.3%
o 1
14.3%
r 1
14.3%
e 1
14.3%
a 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 1
14.3%
i 1
14.3%
n 1
14.3%
o 1
14.3%
r 1
14.3%
e 1
14.3%
a 1
14.3%
Distinct171484
Distinct (%)17.3%
Missing3
Missing (%)< 0.1%
Memory size7.5 MiB
2025-01-07T10:43:33.621659image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length145
Median length90
Mean length31.1400224
Min length5

Characters and Unicode

Total characters30778767
Distinct characters118
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique76155 ?
Unique (%)7.7%

Sample

1st rowLithothamnion calcareum (Pallas) Areschoug
2nd rowAmicia glandulosa Kunth
3rd rowTripogandra glandulosa (Seub.) Rohweder
4th rowConnarus steyermarkii Prance
5th rowTrichoneura grandiglumis (Nees) Ekman
ValueCountFrequency (%)
l 155063
 
4.1%
123403
 
3.2%
ex 71536
 
1.9%
var 42961
 
1.1%
kunth 25715
 
0.7%
dc 25369
 
0.7%
benth 22482
 
0.6%
a.gray 22453
 
0.6%
subsp 20360
 
0.5%
sw 19134
 
0.5%
Other values (72296) 3270675
86.1%
2025-01-07T10:43:33.923241image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2810752
 
9.1%
a 2784580
 
9.0%
i 2163680
 
7.0%
e 1918469
 
6.2%
r 1705055
 
5.5%
l 1505257
 
4.9%
o 1502652
 
4.9%
n 1425654
 
4.6%
s 1389295
 
4.5%
. 1385249
 
4.5%
Other values (108) 12188124
39.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30778767
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2810752
 
9.1%
a 2784580
 
9.0%
i 2163680
 
7.0%
e 1918469
 
6.2%
r 1705055
 
5.5%
l 1505257
 
4.9%
o 1502652
 
4.9%
n 1425654
 
4.6%
s 1389295
 
4.5%
. 1385249
 
4.5%
Other values (108) 12188124
39.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30778767
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2810752
 
9.1%
a 2784580
 
9.0%
i 2163680
 
7.0%
e 1918469
 
6.2%
r 1705055
 
5.5%
l 1505257
 
4.9%
o 1502652
 
4.9%
n 1425654
 
4.6%
s 1389295
 
4.5%
. 1385249
 
4.5%
Other values (108) 12188124
39.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30778767
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2810752
 
9.1%
a 2784580
 
9.0%
i 2163680
 
7.0%
e 1918469
 
6.2%
r 1705055
 
5.5%
l 1505257
 
4.9%
o 1502652
 
4.9%
n 1425654
 
4.6%
s 1389295
 
4.5%
. 1385249
 
4.5%
Other values (108) 12188124
39.6%

acceptedNameUsage
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

parentNameUsage
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Memory size7.5 MiB
2025-01-07T10:43:33.977242image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters9
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowpubiflora
ValueCountFrequency (%)
pubiflora 1
100.0%
2025-01-07T10:43:34.067308image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
p 1
11.1%
u 1
11.1%
b 1
11.1%
i 1
11.1%
f 1
11.1%
l 1
11.1%
o 1
11.1%
r 1
11.1%
a 1
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p 1
11.1%
u 1
11.1%
b 1
11.1%
i 1
11.1%
f 1
11.1%
l 1
11.1%
o 1
11.1%
r 1
11.1%
a 1
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p 1
11.1%
u 1
11.1%
b 1
11.1%
i 1
11.1%
f 1
11.1%
l 1
11.1%
o 1
11.1%
r 1
11.1%
a 1
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p 1
11.1%
u 1
11.1%
b 1
11.1%
i 1
11.1%
f 1
11.1%
l 1
11.1%
o 1
11.1%
r 1
11.1%
a 1
11.1%

originalNameUsage
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Memory size7.5 MiB
2025-01-07T10:43:34.109224image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters9
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowpubiflora
ValueCountFrequency (%)
pubiflora 1
100.0%
2025-01-07T10:43:34.280962image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
p 1
11.1%
u 1
11.1%
b 1
11.1%
i 1
11.1%
f 1
11.1%
l 1
11.1%
o 1
11.1%
r 1
11.1%
a 1
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p 1
11.1%
u 1
11.1%
b 1
11.1%
i 1
11.1%
f 1
11.1%
l 1
11.1%
o 1
11.1%
r 1
11.1%
a 1
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p 1
11.1%
u 1
11.1%
b 1
11.1%
i 1
11.1%
f 1
11.1%
l 1
11.1%
o 1
11.1%
r 1
11.1%
a 1
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p 1
11.1%
u 1
11.1%
b 1
11.1%
i 1
11.1%
f 1
11.1%
l 1
11.1%
o 1
11.1%
r 1
11.1%
a 1
11.1%

nameAccordingTo
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

namePublishedIn
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Memory size7.5 MiB
2025-01-07T10:43:34.323252image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters7
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowVARIETY
ValueCountFrequency (%)
variety 1
100.0%
2025-01-07T10:43:34.415874image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
V 1
14.3%
A 1
14.3%
R 1
14.3%
I 1
14.3%
E 1
14.3%
T 1
14.3%
Y 1
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
V 1
14.3%
A 1
14.3%
R 1
14.3%
I 1
14.3%
E 1
14.3%
T 1
14.3%
Y 1
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
V 1
14.3%
A 1
14.3%
R 1
14.3%
I 1
14.3%
E 1
14.3%
T 1
14.3%
Y 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
V 1
14.3%
A 1
14.3%
R 1
14.3%
I 1
14.3%
E 1
14.3%
T 1
14.3%
Y 1
14.3%

namePublishedInYear
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB
Distinct1871
Distinct (%)0.2%
Missing3060
Missing (%)0.3%
Memory size7.5 MiB
2025-01-07T10:43:34.596920image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length106
Median length83
Mean length55.78930767
Min length6

Characters and Unicode

Total characters54971548
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique247 ?
Unique (%)< 0.1%

Sample

1st rowPlantae, Rhodophyta, Corallinales, Lithothamniaceae
2nd rowPlantae, Dicotyledonae, Fabales, Fabaceae, Papilionoideae
3rd rowPlantae, Monocotyledonae, Commelinales, Commelinaceae
4th rowPlantae, Dicotyledonae, Oxalidales, Connaraceae
5th rowPlantae, Monocotyledonae, Poales, Poaceae, Chloridoideae
ValueCountFrequency (%)
plantae 906960
 
19.6%
dicotyledonae 565444
 
12.2%
monocotyledonae 198988
 
4.3%
poales 153711
 
3.3%
poaceae 110119
 
2.4%
asterales 83265
 
1.8%
asteraceae 78409
 
1.7%
asteroideae 62020
 
1.3%
pteridophyte 60609
 
1.3%
lamiales 58285
 
1.3%
Other values (1989) 2357982
50.9%
2025-01-07T10:43:34.859671image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 7742674
14.1%
e 7698551
14.0%
o 4128767
 
7.5%
3650450
 
6.6%
, 3626340
 
6.6%
l 3562838
 
6.5%
n 2796731
 
5.1%
t 2746790
 
5.0%
i 2727025
 
5.0%
c 2484340
 
4.5%
Other values (50) 13807042
25.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54971548
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 7742674
14.1%
e 7698551
14.0%
o 4128767
 
7.5%
3650450
 
6.6%
, 3626340
 
6.6%
l 3562838
 
6.5%
n 2796731
 
5.1%
t 2746790
 
5.0%
i 2727025
 
5.0%
c 2484340
 
4.5%
Other values (50) 13807042
25.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54971548
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 7742674
14.1%
e 7698551
14.0%
o 4128767
 
7.5%
3650450
 
6.6%
, 3626340
 
6.6%
l 3562838
 
6.5%
n 2796731
 
5.1%
t 2746790
 
5.0%
i 2727025
 
5.0%
c 2484340
 
4.5%
Other values (50) 13807042
25.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54971548
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 7742674
14.1%
e 7698551
14.0%
o 4128767
 
7.5%
3650450
 
6.6%
, 3626340
 
6.6%
l 3562838
 
6.5%
n 2796731
 
5.1%
t 2746790
 
5.0%
i 2727025
 
5.0%
c 2484340
 
4.5%
Other values (50) 13807042
25.1%
Distinct7
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Memory size7.5 MiB
2025-01-07T10:43:34.921692image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length14
Median length7
Mean length6.971155373
Min length5

Characters and Unicode

Total characters6890283
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPlantae
2nd rowPlantae
3rd rowPlantae
4th rowPlantae
5th rowPlantae
ValueCountFrequency (%)
plantae 907311
91.5%
fungi 48945
 
4.9%
chromista 17041
 
1.7%
bacteria 11701
 
1.2%
incertae 3366
 
0.3%
sedis 3366
 
0.3%
protozoa 31
 
< 0.1%
animalia 4
 
< 0.1%
2025-01-07T10:43:35.019617image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1858470
27.0%
n 959626
13.9%
t 939450
13.6%
e 929110
13.5%
P 907342
13.2%
l 907315
13.2%
i 84427
 
1.2%
F 48945
 
0.7%
u 48945
 
0.7%
g 48945
 
0.7%
Other values (12) 157708
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6890283
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1858470
27.0%
n 959626
13.9%
t 939450
13.6%
e 929110
13.5%
P 907342
13.2%
l 907315
13.2%
i 84427
 
1.2%
F 48945
 
0.7%
u 48945
 
0.7%
g 48945
 
0.7%
Other values (12) 157708
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6890283
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1858470
27.0%
n 959626
13.9%
t 939450
13.6%
e 929110
13.5%
P 907342
13.2%
l 907315
13.2%
i 84427
 
1.2%
F 48945
 
0.7%
u 48945
 
0.7%
g 48945
 
0.7%
Other values (12) 157708
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6890283
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1858470
27.0%
n 959626
13.9%
t 939450
13.6%
e 929110
13.5%
P 907342
13.2%
l 907315
13.2%
i 84427
 
1.2%
F 48945
 
0.7%
u 48945
 
0.7%
g 48945
 
0.7%
Other values (12) 157708
 
2.3%

phylum
Text

Distinct24
Distinct (%)< 0.1%
Missing4754
Missing (%)0.5%
Memory size7.5 MiB
2025-01-07T10:43:35.072616image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length16
Median length12
Mean length11.72722051
Min length7

Characters and Unicode

Total characters11535457
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowRhodophyta
2nd rowTracheophyta
3rd rowTracheophyta
4th rowTracheophyta
5th rowTracheophyta
ValueCountFrequency (%)
tracheophyta 830617
84.4%
ascomycota 48276
 
4.9%
bryophyta 32695
 
3.3%
rhodophyta 26385
 
2.7%
ochrophyta 15149
 
1.5%
cyanobacteria 11694
 
1.2%
chlorophyta 9268
 
0.9%
marchantiophyta 5937
 
0.6%
myzozoa 1887
 
0.2%
charophyta 1126
 
0.1%
Other values (14) 614
 
0.1%
2025-01-07T10:43:35.177657image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1851109
16.0%
h 1809899
15.7%
o 1070187
9.3%
y 1016309
8.8%
t 987912
8.6%
c 960529
8.3%
p 921305
8.0%
r 906622
7.9%
e 842467
7.3%
T 830618
7.2%
Other values (22) 338500
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11535457
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1851109
16.0%
h 1809899
15.7%
o 1070187
9.3%
y 1016309
8.8%
t 987912
8.6%
c 960529
8.3%
p 921305
8.0%
r 906622
7.9%
e 842467
7.3%
T 830618
7.2%
Other values (22) 338500
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11535457
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1851109
16.0%
h 1809899
15.7%
o 1070187
9.3%
y 1016309
8.8%
t 987912
8.6%
c 960529
8.3%
p 921305
8.0%
r 906622
7.9%
e 842467
7.3%
T 830618
7.2%
Other values (22) 338500
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11535457
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1851109
16.0%
h 1809899
15.7%
o 1070187
9.3%
y 1016309
8.8%
t 987912
8.6%
c 960529
8.3%
p 921305
8.0%
r 906622
7.9%
e 842467
7.3%
T 830618
7.2%
Other values (22) 338500
 
2.9%

class
Text

Distinct68
Distinct (%)< 0.1%
Missing5481
Missing (%)0.6%
Memory size7.5 MiB
2025-01-07T10:43:35.250674image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length19
Median length13
Mean length12.51019767
Min length6

Characters and Unicode

Total characters12296536
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)< 0.1%

Sample

1st rowFlorideophyceae
2nd rowMagnoliopsida
3rd rowLiliopsida
4th rowMagnoliopsida
5th rowLiliopsida
ValueCountFrequency (%)
magnoliopsida 565617
57.5%
liliopsida 199036
 
20.2%
polypodiopsida 54963
 
5.6%
lecanoromycetes 44421
 
4.5%
bryopsida 29396
 
3.0%
florideophyceae 25770
 
2.6%
cyanobacteriia 11282
 
1.1%
bacillariophyceae 8448
 
0.9%
ulvophyceae 8422
 
0.9%
phaeophyceae 6544
 
0.7%
Other values (58) 29022
 
3.0%
2025-01-07T10:43:35.390012image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 1977833
16.1%
o 1746862
14.2%
a 1601551
13.0%
p 985390
8.0%
d 957361
7.8%
s 918228
7.5%
l 873356
7.1%
n 649522
 
5.3%
g 573852
 
4.7%
M 566314
 
4.6%
Other values (32) 1446267
11.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12296536
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 1977833
16.1%
o 1746862
14.2%
a 1601551
13.0%
p 985390
8.0%
d 957361
7.8%
s 918228
7.5%
l 873356
7.1%
n 649522
 
5.3%
g 573852
 
4.7%
M 566314
 
4.6%
Other values (32) 1446267
11.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12296536
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 1977833
16.1%
o 1746862
14.2%
a 1601551
13.0%
p 985390
8.0%
d 957361
7.8%
s 918228
7.5%
l 873356
7.1%
n 649522
 
5.3%
g 573852
 
4.7%
M 566314
 
4.6%
Other values (32) 1446267
11.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12296536
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 1977833
16.1%
o 1746862
14.2%
a 1601551
13.0%
p 985390
8.0%
d 957361
7.8%
s 918228
7.5%
l 873356
7.1%
n 649522
 
5.3%
g 573852
 
4.7%
M 566314
 
4.6%
Other values (32) 1446267
11.8%

order
Text

Missing 

Distinct357
Distinct (%)< 0.1%
Missing10135
Missing (%)1.0%
Memory size7.5 MiB
2025-01-07T10:43:35.568100image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length21
Median length18
Mean length9.357003763
Min length6

Characters and Unicode

Total characters9153648
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)< 0.1%

Sample

1st rowCorallinales
2nd rowFabales
3rd rowCommelinales
4th rowOxalidales
5th rowPoales
ValueCountFrequency (%)
poales 153750
 
15.7%
asterales 83320
 
8.5%
lamiales 58318
 
6.0%
fabales 55218
 
5.6%
malpighiales 46323
 
4.7%
polypodiales 42295
 
4.3%
gentianales 39541
 
4.0%
myrtales 34933
 
3.6%
caryophyllales 32482
 
3.3%
rosales 28326
 
2.9%
Other values (347) 403761
41.3%
2025-01-07T10:43:35.816243image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1510235
16.5%
l 1284489
14.0%
e 1227651
13.4%
s 1170802
12.8%
i 502041
 
5.5%
o 443593
 
4.8%
r 374076
 
4.1%
n 276338
 
3.0%
t 238442
 
2.6%
P 224898
 
2.5%
Other values (39) 1901083
20.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9153648
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1510235
16.5%
l 1284489
14.0%
e 1227651
13.4%
s 1170802
12.8%
i 502041
 
5.5%
o 443593
 
4.8%
r 374076
 
4.1%
n 276338
 
3.0%
t 238442
 
2.6%
P 224898
 
2.5%
Other values (39) 1901083
20.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9153648
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1510235
16.5%
l 1284489
14.0%
e 1227651
13.4%
s 1170802
12.8%
i 502041
 
5.5%
o 443593
 
4.8%
r 374076
 
4.1%
n 276338
 
3.0%
t 238442
 
2.6%
P 224898
 
2.5%
Other values (39) 1901083
20.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9153648
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1510235
16.5%
l 1284489
14.0%
e 1227651
13.4%
s 1170802
12.8%
i 502041
 
5.5%
o 443593
 
4.8%
r 374076
 
4.1%
n 276338
 
3.0%
t 238442
 
2.6%
P 224898
 
2.5%
Other values (39) 1901083
20.8%

superfamily
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Memory size7.5 MiB
2025-01-07T10:43:35.880244image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters36
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row821cc27a-e3bb-4bc5-ac34-89ada245069d
ValueCountFrequency (%)
821cc27a-e3bb-4bc5-ac34-89ada245069d 1
100.0%
2025-01-07T10:43:35.988390image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 4
11.1%
a 4
11.1%
- 4
11.1%
2 3
8.3%
4 3
8.3%
b 3
8.3%
8 2
 
5.6%
3 2
 
5.6%
9 2
 
5.6%
d 2
 
5.6%
Other values (6) 7
19.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 4
11.1%
a 4
11.1%
- 4
11.1%
2 3
8.3%
4 3
8.3%
b 3
8.3%
8 2
 
5.6%
3 2
 
5.6%
9 2
 
5.6%
d 2
 
5.6%
Other values (6) 7
19.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 4
11.1%
a 4
11.1%
- 4
11.1%
2 3
8.3%
4 3
8.3%
b 3
8.3%
8 2
 
5.6%
3 2
 
5.6%
9 2
 
5.6%
d 2
 
5.6%
Other values (6) 7
19.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 4
11.1%
a 4
11.1%
- 4
11.1%
2 3
8.3%
4 3
8.3%
b 3
8.3%
8 2
 
5.6%
3 2
 
5.6%
9 2
 
5.6%
d 2
 
5.6%
Other values (6) 7
19.4%

family
Text

Missing 

Distinct1293
Distinct (%)0.1%
Missing10432
Missing (%)1.1%
Memory size7.5 MiB
2025-01-07T10:43:36.146789image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length23
Median length20
Mean length10.76219925
Min length2

Characters and Unicode

Total characters10525108
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique122 ?
Unique (%)< 0.1%

Sample

1st rowHapalidiaceae
2nd rowFabaceae
3rd rowCommelinaceae
4th rowConnaraceae
5th rowPoaceae
ValueCountFrequency (%)
poaceae 110118
 
11.3%
asteraceae 78427
 
8.0%
fabaceae 51638
 
5.3%
cyperaceae 30498
 
3.1%
rubiaceae 26201
 
2.7%
melastomataceae 16271
 
1.7%
malvaceae 14761
 
1.5%
rosaceae 14530
 
1.5%
parmeliaceae 14370
 
1.5%
lamiaceae 13720
 
1.4%
Other values (1283) 607436
62.1%
2025-01-07T10:43:36.378371image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2413737
22.9%
e 2328328
22.1%
c 1165771
11.1%
i 467905
 
4.4%
r 452986
 
4.3%
o 448954
 
4.3%
l 342205
 
3.3%
t 293841
 
2.8%
n 274374
 
2.6%
s 219276
 
2.1%
Other values (42) 2117731
20.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10525108
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2413737
22.9%
e 2328328
22.1%
c 1165771
11.1%
i 467905
 
4.4%
r 452986
 
4.3%
o 448954
 
4.3%
l 342205
 
3.3%
t 293841
 
2.8%
n 274374
 
2.6%
s 219276
 
2.1%
Other values (42) 2117731
20.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10525108
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2413737
22.9%
e 2328328
22.1%
c 1165771
11.1%
i 467905
 
4.4%
r 452986
 
4.3%
o 448954
 
4.3%
l 342205
 
3.3%
t 293841
 
2.8%
n 274374
 
2.6%
s 219276
 
2.1%
Other values (42) 2117731
20.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10525108
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2413737
22.9%
e 2328328
22.1%
c 1165771
11.1%
i 467905
 
4.4%
r 452986
 
4.3%
o 448954
 
4.3%
l 342205
 
3.3%
t 293841
 
2.8%
n 274374
 
2.6%
s 219276
 
2.1%
Other values (42) 2117731
20.1%

subfamily
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Memory size7.5 MiB
2025-01-07T10:43:36.443903image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters24
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row2024-12-02T13:57:09.776Z
ValueCountFrequency (%)
2024-12-02t13:57:09.776z 1
100.0%
2025-01-07T10:43:36.544818image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 4
16.7%
0 3
12.5%
7 3
12.5%
- 2
8.3%
: 2
8.3%
1 2
8.3%
T 1
 
4.2%
4 1
 
4.2%
3 1
 
4.2%
5 1
 
4.2%
Other values (4) 4
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 4
16.7%
0 3
12.5%
7 3
12.5%
- 2
8.3%
: 2
8.3%
1 2
8.3%
T 1
 
4.2%
4 1
 
4.2%
3 1
 
4.2%
5 1
 
4.2%
Other values (4) 4
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 4
16.7%
0 3
12.5%
7 3
12.5%
- 2
8.3%
: 2
8.3%
1 2
8.3%
T 1
 
4.2%
4 1
 
4.2%
3 1
 
4.2%
5 1
 
4.2%
Other values (4) 4
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 4
16.7%
0 3
12.5%
7 3
12.5%
- 2
8.3%
: 2
8.3%
1 2
8.3%
T 1
 
4.2%
4 1
 
4.2%
3 1
 
4.2%
5 1
 
4.2%
Other values (4) 4
16.7%

tribe
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean450
Minimum450
Maximum450
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:36.603801image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum450
5-th percentile450
Q1450
median450
Q3450
95-th percentile450
Maximum450
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Mean450
Median Absolute Deviation (MAD)0
Skewnessnan
Sum450
Variancenan
MonotonicityStrictly increasing
2025-01-07T10:43:36.649800image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
450 1
 
< 0.1%
(Missing) 988401
> 99.9%
ValueCountFrequency (%)
450 1
< 0.1%
ValueCountFrequency (%)
450 1
< 0.1%

subtribe
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean50
Minimum50
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:36.693959image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile50
Q150
median50
Q350
95-th percentile50
Maximum50
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Mean50
Median Absolute Deviation (MAD)0
Skewnessnan
Sum50
Variancenan
MonotonicityStrictly increasing
2025-01-07T10:43:36.738468image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
50 1
 
< 0.1%
(Missing) 988401
> 99.9%
ValueCountFrequency (%)
50 1
< 0.1%
ValueCountFrequency (%)
50 1
< 0.1%

genus
Text

Missing 

Distinct14195
Distinct (%)1.5%
Missing15345
Missing (%)1.6%
Memory size7.5 MiB
2025-01-07T10:43:36.924355image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length22
Median length19
Mean length8.8481127
Min length2

Characters and Unicode

Total characters8609718
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2072 ?
Unique (%)0.2%

Sample

1st rowPhymatolithon
2nd rowAmicia
3rd rowCallisia
4th rowConnarus
5th rowTrichoneura
ValueCountFrequency (%)
carex 12742
 
1.3%
miconia 8772
 
0.9%
cladonia 6873
 
0.7%
poa 6684
 
0.7%
cyperus 6044
 
0.6%
paspalum 5820
 
0.6%
solanum 5538
 
0.6%
eragrostis 5205
 
0.5%
dichanthelium 4464
 
0.5%
asplenium 4297
 
0.4%
Other values (14184) 906618
93.2%
2025-01-07T10:43:37.204555image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1065944
 
12.4%
i 802101
 
9.3%
o 610885
 
7.1%
e 599768
 
7.0%
r 564074
 
6.6%
l 476062
 
5.5%
s 450892
 
5.2%
n 446312
 
5.2%
u 428361
 
5.0%
t 360702
 
4.2%
Other values (43) 2804617
32.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8609718
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1065944
 
12.4%
i 802101
 
9.3%
o 610885
 
7.1%
e 599768
 
7.0%
r 564074
 
6.6%
l 476062
 
5.5%
s 450892
 
5.2%
n 446312
 
5.2%
u 428361
 
5.0%
t 360702
 
4.2%
Other values (43) 2804617
32.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8609718
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1065944
 
12.4%
i 802101
 
9.3%
o 610885
 
7.1%
e 599768
 
7.0%
r 564074
 
6.6%
l 476062
 
5.5%
s 450892
 
5.2%
n 446312
 
5.2%
u 428361
 
5.0%
t 360702
 
4.2%
Other values (43) 2804617
32.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8609718
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1065944
 
12.4%
i 802101
 
9.3%
o 610885
 
7.1%
e 599768
 
7.0%
r 564074
 
6.6%
l 476062
 
5.5%
s 450892
 
5.2%
n 446312
 
5.2%
u 428361
 
5.0%
t 360702
 
4.2%
Other values (43) 2804617
32.6%

genericName
Text

Missing 

Distinct15150
Distinct (%)1.6%
Missing15400
Missing (%)1.6%
Memory size7.5 MiB
2025-01-07T10:43:37.388503image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length20
Median length17
Mean length8.785601674
Min length2

Characters and Unicode

Total characters8548408
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2933 ?
Unique (%)0.3%

Sample

1st rowLithothamnion
2nd rowAmicia
3rd rowTripogandra
4th rowConnarus
5th rowTrichoneura
ValueCountFrequency (%)
carex 12732
 
1.3%
poa 6687
 
0.7%
cyperus 6038
 
0.6%
cladonia 5891
 
0.6%
paspalum 5802
 
0.6%
miconia 5466
 
0.6%
solanum 5416
 
0.6%
eragrostis 5200
 
0.5%
asplenium 4423
 
0.5%
dichanthelium 4230
 
0.4%
Other values (15139) 911117
93.6%
2025-01-07T10:43:37.634748image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1056368
 
12.4%
i 790190
 
9.2%
o 599730
 
7.0%
e 592542
 
6.9%
r 561767
 
6.6%
l 470384
 
5.5%
s 445526
 
5.2%
n 443561
 
5.2%
u 432434
 
5.1%
t 358837
 
4.2%
Other values (44) 2797069
32.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8548408
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1056368
 
12.4%
i 790190
 
9.2%
o 599730
 
7.0%
e 592542
 
6.9%
r 561767
 
6.6%
l 470384
 
5.5%
s 445526
 
5.2%
n 443561
 
5.2%
u 432434
 
5.1%
t 358837
 
4.2%
Other values (44) 2797069
32.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8548408
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1056368
 
12.4%
i 790190
 
9.2%
o 599730
 
7.0%
e 592542
 
6.9%
r 561767
 
6.6%
l 470384
 
5.5%
s 445526
 
5.2%
n 443561
 
5.2%
u 432434
 
5.1%
t 358837
 
4.2%
Other values (44) 2797069
32.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8548408
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1056368
 
12.4%
i 790190
 
9.2%
o 599730
 
7.0%
e 592542
 
6.9%
r 561767
 
6.6%
l 470384
 
5.5%
s 445526
 
5.2%
n 443561
 
5.2%
u 432434
 
5.1%
t 358837
 
4.2%
Other values (44) 2797069
32.7%

subgenus
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

infragenericEpithet
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Memory size7.5 MiB
2025-01-07T10:43:37.701977image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length48
Median length48
Mean length48
Min length48

Characters and Unicode

Total characters48
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowOCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_COUNT
ValueCountFrequency (%)
occurrence_status_inferred_from_individual_count 1
100.0%
2025-01-07T10:43:37.811983image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 5
10.4%
_ 5
10.4%
U 4
8.3%
C 4
8.3%
N 4
8.3%
E 4
8.3%
I 4
8.3%
O 3
 
6.2%
D 3
 
6.2%
T 3
 
6.2%
Other values (6) 9
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 48
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 5
10.4%
_ 5
10.4%
U 4
8.3%
C 4
8.3%
N 4
8.3%
E 4
8.3%
I 4
8.3%
O 3
 
6.2%
D 3
 
6.2%
T 3
 
6.2%
Other values (6) 9
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 48
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 5
10.4%
_ 5
10.4%
U 4
8.3%
C 4
8.3%
N 4
8.3%
E 4
8.3%
I 4
8.3%
O 3
 
6.2%
D 3
 
6.2%
T 3
 
6.2%
Other values (6) 9
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 48
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 5
10.4%
_ 5
10.4%
U 4
8.3%
C 4
8.3%
N 4
8.3%
E 4
8.3%
I 4
8.3%
O 3
 
6.2%
D 3
 
6.2%
T 3
 
6.2%
Other values (6) 9
18.8%

specificEpithet
Text

Missing 

Distinct44923
Distinct (%)4.9%
Missing75483
Missing (%)7.6%
Memory size7.5 MiB
2025-01-07T10:43:38.016091image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length25
Median length22
Mean length9.15062344
Min length3

Characters and Unicode

Total characters8353778
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14847 ?
Unique (%)1.6%

Sample

1st rowcalcareum
2nd rowglandulosa
3rd rowglandulosa
4th rowsteyermarkii
5th rowgrandiglumis
ValueCountFrequency (%)
canadensis 2613
 
0.3%
guianensis 2604
 
0.3%
americana 2509
 
0.3%
latifolia 2449
 
0.3%
parviflora 2235
 
0.2%
repens 2200
 
0.2%
gracilis 2040
 
0.2%
occidentalis 2004
 
0.2%
indica 1946
 
0.2%
pubescens 1937
 
0.2%
Other values (44913) 890382
97.5%
2025-01-07T10:43:38.289629image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1131933
13.5%
i 964476
11.5%
s 606683
 
7.3%
e 594963
 
7.1%
r 547391
 
6.6%
l 544114
 
6.5%
n 520530
 
6.2%
u 490822
 
5.9%
o 487476
 
5.8%
t 439575
 
5.3%
Other values (22) 2025815
24.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8353778
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1131933
13.5%
i 964476
11.5%
s 606683
 
7.3%
e 594963
 
7.1%
r 547391
 
6.6%
l 544114
 
6.5%
n 520530
 
6.2%
u 490822
 
5.9%
o 487476
 
5.8%
t 439575
 
5.3%
Other values (22) 2025815
24.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8353778
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1131933
13.5%
i 964476
11.5%
s 606683
 
7.3%
e 594963
 
7.1%
r 547391
 
6.6%
l 544114
 
6.5%
n 520530
 
6.2%
u 490822
 
5.9%
o 487476
 
5.8%
t 439575
 
5.3%
Other values (22) 2025815
24.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8353778
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1131933
13.5%
i 964476
11.5%
s 606683
 
7.3%
e 594963
 
7.1%
r 547391
 
6.6%
l 544114
 
6.5%
n 520530
 
6.2%
u 490822
 
5.9%
o 487476
 
5.8%
t 439575
 
5.3%
Other values (22) 2025815
24.3%

infraspecificEpithet
Text

Missing 

Distinct6984
Distinct (%)10.8%
Missing923675
Missing (%)93.5%
Memory size7.5 MiB
2025-01-07T10:43:38.473772image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length25
Median length19
Mean length9.201986806
Min length4

Characters and Unicode

Total characters595617
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2531 ?
Unique (%)3.9%

Sample

1st rowoxyphylla
2nd rowsubalpinum
3rd rowpubescens
4th rowhirsuta
5th rowcrispa
ValueCountFrequency (%)
acuminatum 942
 
1.5%
pubescens 386
 
0.6%
secunda 352
 
0.5%
dichotomum 328
 
0.5%
gracilis 322
 
0.5%
americana 321
 
0.5%
angustifolia 270
 
0.4%
glauca 264
 
0.4%
occidentalis 234
 
0.4%
mexicana 225
 
0.3%
Other values (6974) 61083
94.4%
2025-01-07T10:43:38.715050image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 81071
13.6%
i 68606
11.5%
s 43652
 
7.3%
e 41408
 
7.0%
l 40211
 
6.8%
n 37437
 
6.3%
r 36574
 
6.1%
u 35997
 
6.0%
o 33783
 
5.7%
t 30496
 
5.1%
Other values (18) 146382
24.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 595617
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 81071
13.6%
i 68606
11.5%
s 43652
 
7.3%
e 41408
 
7.0%
l 40211
 
6.8%
n 37437
 
6.3%
r 36574
 
6.1%
u 35997
 
6.0%
o 33783
 
5.7%
t 30496
 
5.1%
Other values (18) 146382
24.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 595617
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 81071
13.6%
i 68606
11.5%
s 43652
 
7.3%
e 41408
 
7.0%
l 40211
 
6.8%
n 37437
 
6.3%
r 36574
 
6.1%
u 35997
 
6.0%
o 33783
 
5.7%
t 30496
 
5.1%
Other values (18) 146382
24.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 595617
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 81071
13.6%
i 68606
11.5%
s 43652
 
7.3%
e 41408
 
7.0%
l 40211
 
6.8%
n 37437
 
6.3%
r 36574
 
6.1%
u 35997
 
6.0%
o 33783
 
5.7%
t 30496
 
5.1%
Other values (18) 146382
24.6%

cultivarEpithet
Boolean

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Memory size7.5 MiB
False
 
1
(Missing)
988401 
ValueCountFrequency (%)
False 1
 
< 0.1%
(Missing) 988401
> 99.9%
2025-01-07T10:43:38.888236image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Distinct11
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size7.5 MiB
2025-01-07T10:43:38.923237image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length7
Mean length6.92467928
Min length4

Characters and Unicode

Total characters6844353
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSPECIES
2nd rowSPECIES
3rd rowSPECIES
4th rowSPECIES
5th rowSPECIES
ValueCountFrequency (%)
species 848247
85.8%
genus 60084
 
6.1%
variety 42962
 
4.3%
subspecies 20363
 
2.1%
family 5330
 
0.5%
kingdom 4747
 
0.5%
phylum 4695
 
0.5%
form 1401
 
0.1%
class 501
 
0.1%
order 69
 
< 0.1%
2025-01-07T10:43:39.030755image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 1840335
26.9%
S 1818669
26.6%
I 921649
13.5%
P 873305
12.8%
C 869111
12.7%
U 85142
 
1.2%
G 64831
 
0.9%
N 64831
 
0.9%
Y 52987
 
0.8%
A 48793
 
0.7%
Other values (17) 204700
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6844353
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 1840335
26.9%
S 1818669
26.6%
I 921649
13.5%
P 873305
12.8%
C 869111
12.7%
U 85142
 
1.2%
G 64831
 
0.9%
N 64831
 
0.9%
Y 52987
 
0.8%
A 48793
 
0.7%
Other values (17) 204700
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6844353
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 1840335
26.9%
S 1818669
26.6%
I 921649
13.5%
P 873305
12.8%
C 869111
12.7%
U 85142
 
1.2%
G 64831
 
0.9%
N 64831
 
0.9%
Y 52987
 
0.8%
A 48793
 
0.7%
Other values (17) 204700
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6844353
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 1840335
26.9%
S 1818669
26.6%
I 921649
13.5%
P 873305
12.8%
C 869111
12.7%
U 85142
 
1.2%
G 64831
 
0.9%
N 64831
 
0.9%
Y 52987
 
0.8%
A 48793
 
0.7%
Other values (17) 204700
 
3.0%

verbatimTaxonRank
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean7296210
Minimum7296210
Maximum7296210
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:39.085160image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum7296210
5-th percentile7296210
Q17296210
median7296210
Q37296210
95-th percentile7296210
Maximum7296210
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Mean7296210
Median Absolute Deviation (MAD)0
Skewnessnan
Sum7296210
Variancenan
MonotonicityStrictly increasing
2025-01-07T10:43:39.130221image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
7296210 1
 
< 0.1%
(Missing) 988401
> 99.9%
ValueCountFrequency (%)
7296210 1
< 0.1%
ValueCountFrequency (%)
7296210 1
< 0.1%

vernacularName
Unsupported

Missing  Rejected  Unsupported 

Missing988400
Missing (%)> 99.9%
Memory size7.5 MiB

nomenclaturalCode
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean7707728
Minimum7707728
Maximum7707728
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:39.173218image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum7707728
5-th percentile7707728
Q17707728
median7707728
Q37707728
95-th percentile7707728
Maximum7707728
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Mean7707728
Median Absolute Deviation (MAD)0
Skewnessnan
Sum7707728
Variancenan
MonotonicityStrictly increasing
2025-01-07T10:43:39.217215image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
7707728 1
 
< 0.1%
(Missing) 988401
> 99.9%
ValueCountFrequency (%)
7707728 1
< 0.1%
ValueCountFrequency (%)
7707728 1
< 0.1%
Distinct4
Distinct (%)< 0.1%
Missing3368
Missing (%)0.3%
Memory size7.5 MiB
2025-01-07T10:43:39.252214image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.802124597
Min length3

Characters and Unicode

Total characters7685358
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSYNONYM
2nd rowACCEPTED
3rd rowSYNONYM
4th rowACCEPTED
5th rowACCEPTED
ValueCountFrequency (%)
accepted 779014
79.1%
synonym 194909
 
19.8%
doubtful 11110
 
1.1%
220 1
 
< 0.1%
2025-01-07T10:43:39.351104image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 1558028
20.3%
E 1558028
20.3%
T 790124
10.3%
D 790124
10.3%
A 779014
10.1%
P 779014
10.1%
Y 389818
 
5.1%
N 389818
 
5.1%
O 206019
 
2.7%
S 194909
 
2.5%
Other values (7) 250462
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7685358
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 1558028
20.3%
E 1558028
20.3%
T 790124
10.3%
D 790124
10.3%
A 779014
10.1%
P 779014
10.1%
Y 389818
 
5.1%
N 389818
 
5.1%
O 206019
 
2.7%
S 194909
 
2.5%
Other values (7) 250462
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7685358
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 1558028
20.3%
E 1558028
20.3%
T 790124
10.3%
D 790124
10.3%
A 779014
10.1%
P 779014
10.1%
Y 389818
 
5.1%
N 389818
 
5.1%
O 206019
 
2.7%
S 194909
 
2.5%
Other values (7) 250462
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7685358
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 1558028
20.3%
E 1558028
20.3%
T 790124
10.3%
D 790124
10.3%
A 779014
10.1%
P 779014
10.1%
Y 389818
 
5.1%
N 389818
 
5.1%
O 206019
 
2.7%
S 194909
 
2.5%
Other values (7) 250462
 
3.3%

nomenclaturalStatus
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean1414
Minimum1414
Maximum1414
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:39.406038image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1414
5-th percentile1414
Q11414
median1414
Q31414
95-th percentile1414
Maximum1414
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Mean1414
Median Absolute Deviation (MAD)0
Skewnessnan
Sum1414
Variancenan
MonotonicityStrictly increasing
2025-01-07T10:43:39.449038image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
1414 1
 
< 0.1%
(Missing) 988401
> 99.9%
ValueCountFrequency (%)
1414 1
< 0.1%
ValueCountFrequency (%)
1414 1
< 0.1%

taxonRemarks
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean6631
Minimum6631
Maximum6631
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:39.493584image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum6631
5-th percentile6631
Q16631
median6631
Q36631
95-th percentile6631
Maximum6631
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Mean6631
Median Absolute Deviation (MAD)0
Skewnessnan
Sum6631
Variancenan
MonotonicityStrictly increasing
2025-01-07T10:43:39.535088image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
6631 1
 
< 0.1%
(Missing) 988401
> 99.9%
ValueCountFrequency (%)
6631 1
< 0.1%
ValueCountFrequency (%)
6631 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size7.5 MiB
2025-01-07T10:43:39.578358image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length36
Median length36
Mean length35.99997066
Min length7

Characters and Unicode

Total characters35582371
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row821cc27a-e3bb-4bc5-ac34-89ada245069d
2nd row821cc27a-e3bb-4bc5-ac34-89ada245069d
3rd row821cc27a-e3bb-4bc5-ac34-89ada245069d
4th row821cc27a-e3bb-4bc5-ac34-89ada245069d
5th row821cc27a-e3bb-4bc5-ac34-89ada245069d
ValueCountFrequency (%)
821cc27a-e3bb-4bc5-ac34-89ada245069d 988399
> 99.9%
7296208 1
 
< 0.1%
2025-01-07T10:43:39.684641image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 3953596
11.1%
a 3953596
11.1%
- 3953596
11.1%
2 2965199
8.3%
4 2965197
8.3%
b 2965197
8.3%
8 1976799
 
5.6%
9 1976799
 
5.6%
5 1976798
 
5.6%
d 1976798
 
5.6%
Other values (6) 6918796
19.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 35582371
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 3953596
11.1%
a 3953596
11.1%
- 3953596
11.1%
2 2965199
8.3%
4 2965197
8.3%
b 2965197
8.3%
8 1976799
 
5.6%
9 1976799
 
5.6%
5 1976798
 
5.6%
d 1976798
 
5.6%
Other values (6) 6918796
19.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 35582371
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 3953596
11.1%
a 3953596
11.1%
- 3953596
11.1%
2 2965199
8.3%
4 2965197
8.3%
b 2965197
8.3%
8 1976799
 
5.6%
9 1976799
 
5.6%
5 1976798
 
5.6%
d 1976798
 
5.6%
Other values (6) 6918796
19.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 35582371
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 3953596
11.1%
a 3953596
11.1%
- 3953596
11.1%
2 2965199
8.3%
4 2965197
8.3%
b 2965197
8.3%
8 1976799
 
5.6%
9 1976799
 
5.6%
5 1976798
 
5.6%
d 1976798
 
5.6%
Other values (6) 6918796
19.4%

publishingCountry
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Memory size7.5 MiB
2025-01-07T10:43:39.724766image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1976798
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowUS
5th rowUS
ValueCountFrequency (%)
us 988399
100.0%
2025-01-07T10:43:39.817532image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 988399
50.0%
S 988399
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1976798
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 988399
50.0%
S 988399
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1976798
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 988399
50.0%
S 988399
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1976798
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 988399
50.0%
S 988399
50.0%
Distinct200353
Distinct (%)20.3%
Missing2
Missing (%)< 0.1%
Memory size7.5 MiB
2025-01-07T10:43:39.965633image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length24
Mean length23.99574565
Min length7

Characters and Unicode

Total characters23717395
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20728 ?
Unique (%)2.1%

Sample

1st row2024-12-02T13:59:14.452Z
2nd row2024-12-02T13:57:49.629Z
3rd row2024-12-02T13:57:49.533Z
4th row2024-12-02T13:59:17.370Z
5th row2024-12-02T13:59:30.710Z
ValueCountFrequency (%)
2024-12-02t13:57:28.323z 24
 
< 0.1%
2024-12-02t13:57:53.831z 24
 
< 0.1%
2024-12-02t13:56:52.667z 24
 
< 0.1%
2024-12-02t13:57:53.200z 23
 
< 0.1%
2024-12-02t13:57:45.207z 23
 
< 0.1%
2024-12-02t13:57:24.579z 23
 
< 0.1%
2024-12-02t13:57:43.276z 23
 
< 0.1%
2024-12-02t13:57:45.844z 23
 
< 0.1%
2024-12-02t13:57:25.039z 22
 
< 0.1%
2024-12-02t13:57:50.630z 22
 
< 0.1%
Other values (200343) 988169
> 99.9%
2025-01-07T10:43:40.181618image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 4511298
19.0%
0 2508199
10.6%
1 2493151
10.5%
- 1976798
8.3%
: 1976798
8.3%
4 1590376
 
6.7%
5 1570391
 
6.6%
3 1563965
 
6.6%
T 988399
 
4.2%
Z 988399
 
4.2%
Other values (5) 3549621
15.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23717395
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 4511298
19.0%
0 2508199
10.6%
1 2493151
10.5%
- 1976798
8.3%
: 1976798
8.3%
4 1590376
 
6.7%
5 1570391
 
6.6%
3 1563965
 
6.6%
T 988399
 
4.2%
Z 988399
 
4.2%
Other values (5) 3549621
15.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23717395
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 4511298
19.0%
0 2508199
10.6%
1 2493151
10.5%
- 1976798
8.3%
: 1976798
8.3%
4 1590376
 
6.7%
5 1570391
 
6.6%
3 1563965
 
6.6%
T 988399
 
4.2%
Z 988399
 
4.2%
Other values (5) 3549621
15.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23717395
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 4511298
19.0%
0 2508199
10.6%
1 2493151
10.5%
- 1976798
8.3%
: 1976798
8.3%
4 1590376
 
6.7%
5 1570391
 
6.6%
3 1563965
 
6.6%
T 988399
 
4.2%
Z 988399
 
4.2%
Other values (5) 3549621
15.0%

elevation
Unsupported

Missing  Rejected  Unsupported 

Missing625728
Missing (%)63.3%
Memory size7.5 MiB

elevationAccuracy
Unsupported

Missing  Rejected  Unsupported 

Missing880635
Missing (%)89.1%
Memory size7.5 MiB

depth
Unsupported

Missing  Rejected  Unsupported 

Missing979722
Missing (%)99.1%
Memory size7.5 MiB

depthAccuracy
Real number (ℝ)

Missing 

Distinct38
Distinct (%)0.5%
Missing980482
Missing (%)99.2%
Infinite0
Infinite (%)0.0%
Mean2.973577652
Minimum0
Maximum100
Zeros430
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:40.257127image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q33
95-th percentile6
Maximum100
Range100
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.092151875
Coefficient of variation (CV)0.70358071
Kurtosis624.1075534
Mean2.973577652
Median Absolute Deviation (MAD)0
Skewness15.0362316
Sum23550.735
Variance4.37709947
MonotonicityNot monotonic
2025-01-07T10:43:40.316754image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
3 4303
 
0.4%
1 646
 
0.1%
1.5 562
 
0.1%
6 519
 
0.1%
0 430
 
< 0.1%
5 409
 
< 0.1%
2.5 208
 
< 0.1%
2 165
 
< 0.1%
4.5 141
 
< 0.1%
0.5 119
 
< 0.1%
Other values (28) 418
 
< 0.1%
(Missing) 980482
99.2%
ValueCountFrequency (%)
0 430
< 0.1%
0.035 1
 
< 0.1%
0.1 1
 
< 0.1%
0.15 1
 
< 0.1%
0.25 21
 
< 0.1%
ValueCountFrequency (%)
100 1
< 0.1%
50 1
< 0.1%
35 1
< 0.1%
24 1
< 0.1%
12.5 1
< 0.1%

distanceFromCentroidInMeters
Unsupported

Missing  Rejected  Unsupported 

Missing987807
Missing (%)99.9%
Memory size7.5 MiB

issue
Text

Distinct228
Distinct (%)< 0.1%
Missing101
Missing (%)< 0.1%
Memory size7.5 MiB
2025-01-07T10:43:40.374949image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length207
Median length48
Mean length55.97726907
Min length17

Characters and Unicode

Total characters55322391
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)< 0.1%

Sample

1st rowOCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_COUNT;GEODETIC_DATUM_ASSUMED_WGS84;CONTINENT_COORDINATE_MISMATCH
2nd rowOCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_COUNT
3rd rowOCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_COUNT
4th rowOCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_COUNT
5th rowOCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_COUNT
ValueCountFrequency (%)
occurrence_status_inferred_from_individual_count 746709
75.6%
occurrence_status_inferred_from_individual_count;geodetic_datum_assumed_wgs84 122708
 
12.4%
occurrence_status_inferred_from_individual_count;taxon_match_higherrank 20378
 
2.1%
occurrence_status_inferred_from_individual_count;recorded_date_mismatch 20160
 
2.0%
occurrence_status_inferred_from_individual_count;continent_derived_from_country;continent_invalid 18108
 
1.8%
occurrence_status_inferred_from_individual_count;continent_country_mismatch 10576
 
1.1%
occurrence_status_inferred_from_individual_count;taxon_match_fuzzy 10400
 
1.1%
occurrence_status_inferred_from_individual_count;geodetic_datum_assumed_wgs84;geodetic_datum_invalid 4482
 
0.5%
occurrence_status_inferred_from_individual_count;geodetic_datum_assumed_wgs84;taxon_match_higherrank 4056
 
0.4%
occurrence_status_inferred_from_individual_count;geodetic_datum_assumed_wgs84;continent_derived_from_coordinates;continent_invalid 3294
 
0.3%
Other values (218) 27430
 
2.8%
2025-01-07T10:43:40.523382image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 5687389
10.3%
R 5152841
9.3%
E 4657864
 
8.4%
I 4362875
 
7.9%
C 4341521
 
7.8%
N 4323540
 
7.8%
U 4305475
 
7.8%
T 3629406
 
6.6%
D 3603366
 
6.5%
O 3347835
 
6.1%
Other values (29) 11910279
21.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 55322391
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
_ 5687389
10.3%
R 5152841
9.3%
E 4657864
 
8.4%
I 4362875
 
7.9%
C 4341521
 
7.8%
N 4323540
 
7.8%
U 4305475
 
7.8%
T 3629406
 
6.6%
D 3603366
 
6.5%
O 3347835
 
6.1%
Other values (29) 11910279
21.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 55322391
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
_ 5687389
10.3%
R 5152841
9.3%
E 4657864
 
8.4%
I 4362875
 
7.9%
C 4341521
 
7.8%
N 4323540
 
7.8%
U 4305475
 
7.8%
T 3629406
 
6.6%
D 3603366
 
6.5%
O 3347835
 
6.1%
Other values (29) 11910279
21.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 55322391
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
_ 5687389
10.3%
R 5152841
9.3%
E 4657864
 
8.4%
I 4362875
 
7.9%
C 4341521
 
7.8%
N 4323540
 
7.8%
U 4305475
 
7.8%
T 3629406
 
6.6%
D 3603366
 
6.5%
O 3347835
 
6.1%
Other values (29) 11910279
21.5%

mediaType
Text

Missing 

Distinct46
Distinct (%)< 0.1%
Missing69371
Missing (%)7.0%
Memory size7.5 MiB
2025-01-07T10:43:40.590941image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length571
Median length10
Mean length10.82667505
Min length10

Characters and Unicode

Total characters9950050
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st rowStillImage
2nd rowStillImage
3rd rowStillImage
4th rowStillImage
5th rowStillImage
ValueCountFrequency (%)
stillimage 864575
94.1%
stillimage;stillimage 50344
 
5.5%
stillimage;stillimage;stillimage 1419
 
0.2%
stillimage;stillimage;stillimage;stillimage 997
 
0.1%
stillimage;stillimage;stillimage;stillimage;stillimage 485
 
0.1%
stillimage;stillimage;stillimage;stillimage;stillimage;stillimage 345
 
< 0.1%
stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage 233
 
< 0.1%
stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage 154
 
< 0.1%
stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage 88
 
< 0.1%
stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage;stillimage 61
 
< 0.1%
Other values (36) 330
 
< 0.1%
2025-01-07T10:43:40.728715image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 1976192
19.9%
S 988096
9.9%
t 988096
9.9%
i 988096
9.9%
I 988096
9.9%
m 988096
9.9%
a 988096
9.9%
g 988096
9.9%
e 988096
9.9%
; 69066
 
0.7%
Other values (12) 24
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9950050
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 1976192
19.9%
S 988096
9.9%
t 988096
9.9%
i 988096
9.9%
I 988096
9.9%
m 988096
9.9%
a 988096
9.9%
g 988096
9.9%
e 988096
9.9%
; 69066
 
0.7%
Other values (12) 24
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9950050
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 1976192
19.9%
S 988096
9.9%
t 988096
9.9%
i 988096
9.9%
I 988096
9.9%
m 988096
9.9%
a 988096
9.9%
g 988096
9.9%
e 988096
9.9%
; 69066
 
0.7%
Other values (12) 24
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9950050
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 1976192
19.9%
S 988096
9.9%
t 988096
9.9%
i 988096
9.9%
I 988096
9.9%
m 988096
9.9%
a 988096
9.9%
g 988096
9.9%
e 988096
9.9%
; 69066
 
0.7%
Other values (12) 24
 
< 0.1%

hasCoordinate
Unsupported

Rejected  Unsupported 

Missing1
Missing (%)< 0.1%
Memory size7.5 MiB

hasGeospatialIssues
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Memory size7.5 MiB
False
983569 
True
 
4830
(Missing)
 
3
ValueCountFrequency (%)
False 983569
99.5%
True 4830
 
0.5%
(Missing) 3
 
< 0.1%
2025-01-07T10:43:40.787703image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

taxonKey
Real number (ℝ)

Distinct171484
Distinct (%)17.3%
Missing3
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean4482158.383
Minimum0
Maximum12390021
Zeros3366
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:40.839773image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2650701
Q12920437
median3775473
Q35421069
95-th percentile8231418.5
Maximum12390021
Range12390021
Interquartile range (IQR)2500632

Descriptive statistics

Standard deviation2040385.02
Coefficient of variation (CV)0.4552237663
Kurtosis0.5172188487
Mean4482158.383
Median Absolute Deviation (MAD)1072230
Skewness0.9701629293
Sum4.430160863 × 1012
Variance4.16317103 × 1012
MonotonicityNot monotonic
2025-01-07T10:43:40.907653image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8176985 3995
 
0.4%
0 3366
 
0.3%
2655370 1333
 
0.1%
6 1163
 
0.1%
3219107 1082
 
0.1%
5426909 1064
 
0.1%
5426949 994
 
0.1%
4270616 933
 
0.1%
2655497 809
 
0.1%
2654437 772
 
0.1%
Other values (171474) 972888
98.4%
ValueCountFrequency (%)
0 3366
0.3%
5 217
 
< 0.1%
6 1163
 
0.1%
7 1
 
< 0.1%
34 12
 
< 0.1%
ValueCountFrequency (%)
12390021 7
< 0.1%
12389572 1
 
< 0.1%
12389286 1
 
< 0.1%
12387721 4
< 0.1%
12387563 1
 
< 0.1%

acceptedTaxonKey
Unsupported

Rejected  Unsupported 

Missing3368
Missing (%)0.3%
Memory size7.5 MiB

kingdomKey
Unsupported

Rejected  Unsupported 

Missing2
Missing (%)< 0.1%
Memory size7.5 MiB

phylumKey
Unsupported

Rejected  Unsupported 

Missing4754
Missing (%)0.5%
Memory size7.5 MiB

classKey
Real number (ℝ)

Distinct68
Distinct (%)< 0.1%
Missing5481
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean686528.375
Minimum116
Maximum12259753
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:40.972652image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum116
5-th percentile180
Q1196
median220
Q3220
95-th percentile7228684
Maximum12259753
Range12259637
Interquartile range (IQR)24

Descriptive statistics

Standard deviation2261682.78
Coefficient of variation (CV)3.29437626
Kurtosis8.425685792
Mean686528.375
Median Absolute Deviation (MAD)0
Skewness3.132453181
Sum6.748031568 × 1011
Variance5.115208998 × 1012
MonotonicityNot monotonic
2025-01-07T10:43:41.038028image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
220 565617
57.2%
196 199036
 
20.1%
7228684 54963
 
5.6%
180 44421
 
4.5%
327 29396
 
3.0%
342 25770
 
2.6%
10774316 11282
 
1.1%
7947184 8448
 
0.9%
195 8422
 
0.9%
7073593 6544
 
0.7%
Other values (58) 29022
 
2.9%
ValueCountFrequency (%)
116 10
 
< 0.1%
125 695
 
0.1%
126 5240
0.5%
127 2
 
< 0.1%
132 117
 
< 0.1%
ValueCountFrequency (%)
12259753 1518
0.2%
12209763 1
 
< 0.1%
12203163 12
 
< 0.1%
10874653 132
 
< 0.1%
10792796 287
 
< 0.1%

orderKey
Unsupported

Missing  Rejected  Unsupported 

Missing10134
Missing (%)1.0%
Memory size7.5 MiB

familyKey
Unsupported

Missing  Rejected  Unsupported 

Missing10432
Missing (%)1.1%
Memory size7.5 MiB

genusKey
Unsupported

Missing  Rejected  Unsupported 

Missing15344
Missing (%)1.6%
Memory size7.5 MiB

subgenusKey
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Memory size7.5 MiB
2025-01-07T10:43:41.081223image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters13
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowMagnoliopsida
ValueCountFrequency (%)
magnoliopsida 1
100.0%
2025-01-07T10:43:41.173417image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2
15.4%
o 2
15.4%
i 2
15.4%
M 1
7.7%
n 1
7.7%
g 1
7.7%
l 1
7.7%
p 1
7.7%
s 1
7.7%
d 1
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2
15.4%
o 2
15.4%
i 2
15.4%
M 1
7.7%
n 1
7.7%
g 1
7.7%
l 1
7.7%
p 1
7.7%
s 1
7.7%
d 1
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2
15.4%
o 2
15.4%
i 2
15.4%
M 1
7.7%
n 1
7.7%
g 1
7.7%
l 1
7.7%
p 1
7.7%
s 1
7.7%
d 1
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2
15.4%
o 2
15.4%
i 2
15.4%
M 1
7.7%
n 1
7.7%
g 1
7.7%
l 1
7.7%
p 1
7.7%
s 1
7.7%
d 1
7.7%

speciesKey
Unsupported

Missing  Rejected  Unsupported 

Missing75442
Missing (%)7.6%
Memory size7.5 MiB

species
Text

Missing 

Distinct126534
Distinct (%)13.9%
Missing75443
Missing (%)7.6%
Memory size7.5 MiB
2025-01-07T10:43:41.393036image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length36
Median length32
Mean length18.99834275
Min length8

Characters and Unicode

Total characters17344708
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46337 ?
Unique (%)5.1%

Sample

1st rowPhymatolithon calcareum
2nd rowAmicia glandulosa
3rd rowCallisia glandulosa
4th rowConnarus steyermarkii
5th rowTrichoneura grandiglumis
ValueCountFrequency (%)
carex 12516
 
0.7%
miconia 8270
 
0.5%
poa 6546
 
0.4%
cladonia 6511
 
0.4%
cyperus 5985
 
0.3%
paspalum 5640
 
0.3%
solanum 5444
 
0.3%
eragrostis 5024
 
0.3%
dichanthelium 4451
 
0.2%
asplenium 4181
 
0.2%
Other values (53483) 1761460
96.5%
2025-01-07T10:43:41.677883image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2129654
 
12.3%
i 1716826
 
9.9%
e 1157262
 
6.7%
r 1076152
 
6.2%
o 1063266
 
6.1%
s 1029428
 
5.9%
l 993778
 
5.7%
n 939467
 
5.4%
913069
 
5.3%
u 892693
 
5.1%
Other values (44) 5433113
31.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17344708
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2129654
 
12.3%
i 1716826
 
9.9%
e 1157262
 
6.7%
r 1076152
 
6.2%
o 1063266
 
6.1%
s 1029428
 
5.9%
l 993778
 
5.7%
n 939467
 
5.4%
913069
 
5.3%
u 892693
 
5.1%
Other values (44) 5433113
31.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17344708
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2129654
 
12.3%
i 1716826
 
9.9%
e 1157262
 
6.7%
r 1076152
 
6.2%
o 1063266
 
6.1%
s 1029428
 
5.9%
l 993778
 
5.7%
n 939467
 
5.4%
913069
 
5.3%
u 892693
 
5.1%
Other values (44) 5433113
31.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17344708
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2129654
 
12.3%
i 1716826
 
9.9%
e 1157262
 
6.7%
r 1076152
 
6.2%
o 1063266
 
6.1%
s 1029428
 
5.9%
l 993778
 
5.7%
n 939467
 
5.4%
913069
 
5.3%
u 892693
 
5.1%
Other values (44) 5433113
31.3%
Distinct141148
Distinct (%)14.3%
Missing3368
Missing (%)0.3%
Memory size7.5 MiB
2025-01-07T10:43:41.982214image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length145
Median length98
Mean length31.85947389
Min length5

Characters and Unicode

Total characters31382665
Distinct characters124
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52484 ?
Unique (%)5.3%

Sample

1st rowPhymatolithon calcareum (Pallas) Adey & D.L.McKibbin
2nd rowAmicia glandulosa Kunth
3rd rowCallisia glandulosa (Seub.) Christenh. & Byng
4th rowConnarus steyermarkii Prance
5th rowTrichoneura grandiglumis (Nees) Ekman
ValueCountFrequency (%)
l 161894
 
4.2%
145790
 
3.8%
ex 72859
 
1.9%
var 29546
 
0.8%
subsp 28283
 
0.7%
kunth 26788
 
0.7%
dc 25626
 
0.7%
benth 22744
 
0.6%
a.gray 22225
 
0.6%
sw 20887
 
0.5%
Other values (67669) 3311496
85.6%
2025-01-07T10:43:42.270965image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2883104
 
9.2%
a 2801876
 
8.9%
i 2180333
 
6.9%
e 1948892
 
6.2%
r 1712734
 
5.5%
o 1536718
 
4.9%
l 1524818
 
4.9%
. 1445546
 
4.6%
n 1442074
 
4.6%
s 1413219
 
4.5%
Other values (114) 12493351
39.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31382665
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2883104
 
9.2%
a 2801876
 
8.9%
i 2180333
 
6.9%
e 1948892
 
6.2%
r 1712734
 
5.5%
o 1536718
 
4.9%
l 1524818
 
4.9%
. 1445546
 
4.6%
n 1442074
 
4.6%
s 1413219
 
4.5%
Other values (114) 12493351
39.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31382665
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2883104
 
9.2%
a 2801876
 
8.9%
i 2180333
 
6.9%
e 1948892
 
6.2%
r 1712734
 
5.5%
o 1536718
 
4.9%
l 1524818
 
4.9%
. 1445546
 
4.6%
n 1442074
 
4.6%
s 1413219
 
4.5%
Other values (114) 12493351
39.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31382665
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2883104
 
9.2%
a 2801876
 
8.9%
i 2180333
 
6.9%
e 1948892
 
6.2%
r 1712734
 
5.5%
o 1536718
 
4.9%
l 1524818
 
4.9%
. 1445546
 
4.6%
n 1442074
 
4.6%
s 1413219
 
4.5%
Other values (114) 12493351
39.8%
Distinct177770
Distinct (%)18.0%
Missing3017
Missing (%)0.3%
Memory size7.5 MiB
2025-01-07T10:43:42.487931image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length125
Median length94
Mean length19.78091812
Min length6

Characters and Unicode

Total characters19491820
Distinct characters88
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique81124 ?
Unique (%)8.2%

Sample

1st rowLithothamnion calcareum
2nd rowAmicia glandulosa
3rd rowTripogandra glandulosa
4th rowConnarus steyermarkii
5th rowTrichoneura grandiglumis
ValueCountFrequency (%)
sp 59300
 
2.8%
var 45918
 
2.2%
subsp 23075
 
1.1%
carex 12732
 
0.6%
indet 9106
 
0.4%
poa 6687
 
0.3%
cyperus 6038
 
0.3%
cladonia 5900
 
0.3%
paspalum 5802
 
0.3%
miconia 5464
 
0.3%
Other values (64311) 1952155
91.6%
2025-01-07T10:43:42.780337image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2343786
 
12.0%
i 1854416
 
9.5%
e 1259869
 
6.5%
s 1219162
 
6.3%
r 1207808
 
6.2%
1146792
 
5.9%
o 1138492
 
5.8%
l 1071612
 
5.5%
n 1027785
 
5.3%
u 997253
 
5.1%
Other values (78) 6224845
31.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19491820
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2343786
 
12.0%
i 1854416
 
9.5%
e 1259869
 
6.5%
s 1219162
 
6.3%
r 1207808
 
6.2%
1146792
 
5.9%
o 1138492
 
5.8%
l 1071612
 
5.5%
n 1027785
 
5.3%
u 997253
 
5.1%
Other values (78) 6224845
31.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19491820
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2343786
 
12.0%
i 1854416
 
9.5%
e 1259869
 
6.5%
s 1219162
 
6.3%
r 1207808
 
6.2%
1146792
 
5.9%
o 1138492
 
5.8%
l 1071612
 
5.5%
n 1027785
 
5.3%
u 997253
 
5.1%
Other values (78) 6224845
31.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19491820
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2343786
 
12.0%
i 1854416
 
9.5%
e 1259869
 
6.5%
s 1219162
 
6.3%
r 1207808
 
6.2%
1146792
 
5.9%
o 1138492
 
5.8%
l 1071612
 
5.5%
n 1027785
 
5.3%
u 997253
 
5.1%
Other values (78) 6224845
31.9%

typifiedName
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

protocol
Text

Constant 

Distinct1
Distinct (%)< 0.1%
Missing3
Missing (%)< 0.1%
Memory size7.5 MiB
2025-01-07T10:43:42.835843image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2965197
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEML
2nd rowEML
3rd rowEML
4th rowEML
5th rowEML
ValueCountFrequency (%)
eml 988399
100.0%
2025-01-07T10:43:42.927138image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 988399
33.3%
M 988399
33.3%
L 988399
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2965197
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 988399
33.3%
M 988399
33.3%
L 988399
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2965197
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 988399
33.3%
M 988399
33.3%
L 988399
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2965197
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 988399
33.3%
M 988399
33.3%
L 988399
33.3%
Distinct200353
Distinct (%)20.3%
Missing2
Missing (%)< 0.1%
Memory size7.5 MiB
2025-01-07T10:43:43.070231image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length24
Mean length23.99574464
Min length6

Characters and Unicode

Total characters23717394
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20728 ?
Unique (%)2.1%

Sample

1st row2024-12-02T13:59:14.452Z
2nd row2024-12-02T13:57:49.629Z
3rd row2024-12-02T13:57:49.533Z
4th row2024-12-02T13:59:17.370Z
5th row2024-12-02T13:59:30.710Z
ValueCountFrequency (%)
2024-12-02t13:57:28.323z 24
 
< 0.1%
2024-12-02t13:57:53.831z 24
 
< 0.1%
2024-12-02t13:56:52.667z 24
 
< 0.1%
2024-12-02t13:57:53.200z 23
 
< 0.1%
2024-12-02t13:57:45.207z 23
 
< 0.1%
2024-12-02t13:57:24.579z 23
 
< 0.1%
2024-12-02t13:57:43.276z 23
 
< 0.1%
2024-12-02t13:57:45.844z 23
 
< 0.1%
2024-12-02t13:57:25.039z 22
 
< 0.1%
2024-12-02t13:57:50.630z 22
 
< 0.1%
Other values (200343) 988169
> 99.9%
2025-01-07T10:43:43.280534image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 4511296
19.0%
0 2508198
10.6%
1 2493151
10.5%
: 1976798
8.3%
- 1976798
8.3%
4 1590376
 
6.7%
5 1570391
 
6.6%
3 1563965
 
6.6%
T 988399
 
4.2%
Z 988399
 
4.2%
Other values (9) 3549623
15.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23717394
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 4511296
19.0%
0 2508198
10.6%
1 2493151
10.5%
: 1976798
8.3%
- 1976798
8.3%
4 1590376
 
6.7%
5 1570391
 
6.6%
3 1563965
 
6.6%
T 988399
 
4.2%
Z 988399
 
4.2%
Other values (9) 3549623
15.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23717394
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 4511296
19.0%
0 2508198
10.6%
1 2493151
10.5%
: 1976798
8.3%
- 1976798
8.3%
4 1590376
 
6.7%
5 1570391
 
6.6%
3 1563965
 
6.6%
T 988399
 
4.2%
Z 988399
 
4.2%
Other values (9) 3549623
15.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23717394
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 4511296
19.0%
0 2508198
10.6%
1 2493151
10.5%
: 1976798
8.3%
- 1976798
8.3%
4 1590376
 
6.7%
5 1570391
 
6.6%
3 1563965
 
6.6%
T 988399
 
4.2%
Z 988399
 
4.2%
Other values (9) 3549623
15.0%
Distinct2
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size7.5 MiB
2025-01-07T10:43:43.342927image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length24
Mean length23.99998381
Min length8

Characters and Unicode

Total characters23721584
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row2024-12-02T11:48:23.416Z
2nd row2024-12-02T11:48:23.416Z
3rd row2024-12-02T11:48:23.416Z
4th row2024-12-02T11:48:23.416Z
5th row2024-12-02T11:48:23.416Z
ValueCountFrequency (%)
2024-12-02t11:48:23.416z 988399
> 99.9%
rollinia 1
 
< 0.1%
2025-01-07T10:43:43.459127image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 4941995
20.8%
1 3953596
16.7%
4 2965197
12.5%
0 1976798
 
8.3%
- 1976798
 
8.3%
: 1976798
 
8.3%
T 988399
 
4.2%
8 988399
 
4.2%
3 988399
 
4.2%
. 988399
 
4.2%
Other values (8) 1976806
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23721584
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 4941995
20.8%
1 3953596
16.7%
4 2965197
12.5%
0 1976798
 
8.3%
- 1976798
 
8.3%
: 1976798
 
8.3%
T 988399
 
4.2%
8 988399
 
4.2%
3 988399
 
4.2%
. 988399
 
4.2%
Other values (8) 1976806
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23721584
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 4941995
20.8%
1 3953596
16.7%
4 2965197
12.5%
0 1976798
 
8.3%
- 1976798
 
8.3%
: 1976798
 
8.3%
T 988399
 
4.2%
8 988399
 
4.2%
3 988399
 
4.2%
. 988399
 
4.2%
Other values (8) 1976806
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23721584
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 4941995
20.8%
1 3953596
16.7%
4 2965197
12.5%
0 1976798
 
8.3%
- 1976798
 
8.3%
: 1976798
 
8.3%
T 988399
 
4.2%
8 988399
 
4.2%
3 988399
 
4.2%
. 988399
 
4.2%
Other values (8) 1976806
8.3%
Distinct2
Distinct (%)< 0.1%
Missing9253
Missing (%)0.9%
Memory size7.5 MiB
True
687928 
False
291221 
(Missing)
 
9253
ValueCountFrequency (%)
True 687928
69.6%
False 291221
29.5%
(Missing) 9253
 
0.9%
2025-01-07T10:43:43.511926image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

relativeOrganismQuantity
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

projectId
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Memory size7.5 MiB
2025-01-07T10:43:43.537928image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters6
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowedulis
ValueCountFrequency (%)
edulis 1
100.0%
2025-01-07T10:43:43.630435image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1
16.7%
d 1
16.7%
u 1
16.7%
l 1
16.7%
i 1
16.7%
s 1
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1
16.7%
d 1
16.7%
u 1
16.7%
l 1
16.7%
i 1
16.7%
s 1
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1
16.7%
d 1
16.7%
u 1
16.7%
l 1
16.7%
i 1
16.7%
s 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1
16.7%
d 1
16.7%
u 1
16.7%
l 1
16.7%
i 1
16.7%
s 1
16.7%

isSequenced
Unsupported

Rejected  Unsupported 

Missing2
Missing (%)< 0.1%
Memory size7.5 MiB

gbifRegion
Text

Missing 

Distinct7
Distinct (%)< 0.1%
Missing19586
Missing (%)2.0%
Memory size7.5 MiB
2025-01-07T10:43:43.683927image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length13
Mean length11.14384878
Min length4

Characters and Unicode

Total characters10796339
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNORTH_AMERICA
2nd rowLATIN_AMERICA
3rd rowLATIN_AMERICA
4th rowLATIN_AMERICA
5th rowAFRICA
ValueCountFrequency (%)
latin_america 416098
42.9%
north_america 317523
32.8%
asia 99994
 
10.3%
europe 56004
 
5.8%
oceania 44344
 
4.6%
africa 33918
 
3.5%
antarctica 935
 
0.1%
2025-01-07T10:43:43.790652image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 2242657
20.8%
I 1328910
12.3%
R 1142001
10.6%
E 889973
 
8.2%
C 813753
 
7.5%
N 778900
 
7.2%
T 735491
 
6.8%
_ 733621
 
6.8%
M 733621
 
6.8%
O 417871
 
3.9%
Other values (6) 979541
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10796339
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 2242657
20.8%
I 1328910
12.3%
R 1142001
10.6%
E 889973
 
8.2%
C 813753
 
7.5%
N 778900
 
7.2%
T 735491
 
6.8%
_ 733621
 
6.8%
M 733621
 
6.8%
O 417871
 
3.9%
Other values (6) 979541
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10796339
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 2242657
20.8%
I 1328910
12.3%
R 1142001
10.6%
E 889973
 
8.2%
C 813753
 
7.5%
N 778900
 
7.2%
T 735491
 
6.8%
_ 733621
 
6.8%
M 733621
 
6.8%
O 417871
 
3.9%
Other values (6) 979541
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10796339
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 2242657
20.8%
I 1328910
12.3%
R 1142001
10.6%
E 889973
 
8.2%
C 813753
 
7.5%
N 778900
 
7.2%
T 735491
 
6.8%
_ 733621
 
6.8%
M 733621
 
6.8%
O 417871
 
3.9%
Other values (6) 979541
9.1%
Distinct2
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size7.5 MiB
2025-01-07T10:43:43.837157image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length13
Mean length12.99999393
Min length7

Characters and Unicode

Total characters12849194
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNORTH_AMERICA
2nd rowNORTH_AMERICA
3rd rowNORTH_AMERICA
4th rowNORTH_AMERICA
5th rowNORTH_AMERICA
ValueCountFrequency (%)
north_america 988399
> 99.9%
variety 1
 
< 0.1%
2025-01-07T10:43:43.942386image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 1976799
15.4%
A 1976799
15.4%
T 988400
7.7%
E 988400
7.7%
I 988400
7.7%
H 988399
7.7%
O 988399
7.7%
N 988399
7.7%
_ 988399
7.7%
M 988399
7.7%
Other values (3) 988401
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12849194
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 1976799
15.4%
A 1976799
15.4%
T 988400
7.7%
E 988400
7.7%
I 988400
7.7%
H 988399
7.7%
O 988399
7.7%
N 988399
7.7%
_ 988399
7.7%
M 988399
7.7%
Other values (3) 988401
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12849194
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 1976799
15.4%
A 1976799
15.4%
T 988400
7.7%
E 988400
7.7%
I 988400
7.7%
H 988399
7.7%
O 988399
7.7%
N 988399
7.7%
_ 988399
7.7%
M 988399
7.7%
Other values (3) 988401
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12849194
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 1976799
15.4%
A 1976799
15.4%
T 988400
7.7%
E 988400
7.7%
I 988400
7.7%
H 988399
7.7%
O 988399
7.7%
N 988399
7.7%
_ 988399
7.7%
M 988399
7.7%
Other values (3) 988401
7.7%

level0Gid
Text

Missing 

Distinct195
Distinct (%)0.1%
Missing854767
Missing (%)86.5%
Memory size7.5 MiB
2025-01-07T10:43:44.096875image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters400905
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)< 0.1%

Sample

1st rowDOM
2nd rowCHL
3rd rowGUY
4th rowUSA
5th rowBRA
ValueCountFrequency (%)
usa 23761
17.8%
guy 14629
 
10.9%
bra 11793
 
8.8%
mex 10743
 
8.0%
ven 10534
 
7.9%
ecu 6689
 
5.0%
guf 4499
 
3.4%
bol 4471
 
3.3%
per 4388
 
3.3%
col 3749
 
2.8%
Other values (185) 38379
28.7%
2025-01-07T10:43:44.303735image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 55471
13.8%
A 49231
12.3%
E 33716
 
8.4%
S 30480
 
7.6%
R 25143
 
6.3%
G 24739
 
6.2%
N 22898
 
5.7%
C 20842
 
5.2%
B 18493
 
4.6%
M 17528
 
4.4%
Other values (19) 102364
25.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 400905
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 55471
13.8%
A 49231
12.3%
E 33716
 
8.4%
S 30480
 
7.6%
R 25143
 
6.3%
G 24739
 
6.2%
N 22898
 
5.7%
C 20842
 
5.2%
B 18493
 
4.6%
M 17528
 
4.4%
Other values (19) 102364
25.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 400905
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 55471
13.8%
A 49231
12.3%
E 33716
 
8.4%
S 30480
 
7.6%
R 25143
 
6.3%
G 24739
 
6.2%
N 22898
 
5.7%
C 20842
 
5.2%
B 18493
 
4.6%
M 17528
 
4.4%
Other values (19) 102364
25.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 400905
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 55471
13.8%
A 49231
12.3%
E 33716
 
8.4%
S 30480
 
7.6%
R 25143
 
6.3%
G 24739
 
6.2%
N 22898
 
5.7%
C 20842
 
5.2%
B 18493
 
4.6%
M 17528
 
4.4%
Other values (19) 102364
25.5%

level0Name
Text

Missing 

Distinct195
Distinct (%)0.1%
Missing854767
Missing (%)86.5%
Memory size7.5 MiB
2025-01-07T10:43:44.495574image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length32
Median length27
Mean length8.588745463
Min length4

Characters and Unicode

Total characters1147757
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)< 0.1%

Sample

1st rowDominican Republic
2nd rowChile
3rd rowGuyana
4th rowUnited States
5th rowBrazil
ValueCountFrequency (%)
united 23809
13.7%
states 23778
13.7%
guyana 14629
 
8.4%
brazil 11793
 
6.8%
méxico 10743
 
6.2%
venezuela 10534
 
6.1%
ecuador 6689
 
3.9%
french 5197
 
3.0%
guiana 4499
 
2.6%
bolivia 4471
 
2.6%
Other values (224) 57483
33.1%
2025-01-07T10:43:44.756883image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 163858
14.3%
e 105662
 
9.2%
i 94961
 
8.3%
n 84634
 
7.4%
t 81902
 
7.1%
u 56381
 
4.9%
r 43581
 
3.8%
o 42775
 
3.7%
39990
 
3.5%
l 39687
 
3.5%
Other values (52) 394326
34.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1147757
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 163858
14.3%
e 105662
 
9.2%
i 94961
 
8.3%
n 84634
 
7.4%
t 81902
 
7.1%
u 56381
 
4.9%
r 43581
 
3.8%
o 42775
 
3.7%
39990
 
3.5%
l 39687
 
3.5%
Other values (52) 394326
34.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1147757
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 163858
14.3%
e 105662
 
9.2%
i 94961
 
8.3%
n 84634
 
7.4%
t 81902
 
7.1%
u 56381
 
4.9%
r 43581
 
3.8%
o 42775
 
3.7%
39990
 
3.5%
l 39687
 
3.5%
Other values (52) 394326
34.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1147757
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 163858
14.3%
e 105662
 
9.2%
i 94961
 
8.3%
n 84634
 
7.4%
t 81902
 
7.1%
u 56381
 
4.9%
r 43581
 
3.8%
o 42775
 
3.7%
39990
 
3.5%
l 39687
 
3.5%
Other values (52) 394326
34.4%

level1Gid
Text

Missing 

Distinct1703
Distinct (%)1.3%
Missing855021
Missing (%)86.5%
Memory size7.5 MiB
2025-01-07T10:43:44.976203image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length8
Median length7
Mean length7.447372564
Min length6

Characters and Unicode

Total characters993338
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique338 ?
Unique (%)0.3%

Sample

1st rowDOM.26_1
2nd rowCHL.6_1
3rd rowGUY.2_1
4th rowUSA.47_1
5th rowBRA.1_1
ValueCountFrequency (%)
usa.21_1 4566
 
3.4%
guy.8_1 4001
 
3.0%
usa.47_1 3961
 
3.0%
guy.10_1 3952
 
3.0%
guy.2_1 3604
 
2.7%
ven.1_1 3448
 
2.6%
usa.9_1 3286
 
2.5%
ven.6_1 3215
 
2.4%
guf.1_1 2886
 
2.2%
usa.2_1 2677
 
2.0%
Other values (1693) 97785
73.3%
2025-01-07T10:43:45.258407image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 178102
17.9%
_ 133380
13.4%
. 133323
13.4%
U 55403
 
5.6%
A 48963
 
4.9%
2 44340
 
4.5%
E 33716
 
3.4%
S 30480
 
3.1%
R 25125
 
2.5%
G 24740
 
2.5%
Other values (28) 285766
28.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 993338
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 178102
17.9%
_ 133380
13.4%
. 133323
13.4%
U 55403
 
5.6%
A 48963
 
4.9%
2 44340
 
4.5%
E 33716
 
3.4%
S 30480
 
3.1%
R 25125
 
2.5%
G 24740
 
2.5%
Other values (28) 285766
28.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 993338
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 178102
17.9%
_ 133380
13.4%
. 133323
13.4%
U 55403
 
5.6%
A 48963
 
4.9%
2 44340
 
4.5%
E 33716
 
3.4%
S 30480
 
3.1%
R 25125
 
2.5%
G 24740
 
2.5%
Other values (28) 285766
28.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 993338
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 178102
17.9%
_ 133380
13.4%
. 133323
13.4%
U 55403
 
5.6%
A 48963
 
4.9%
2 44340
 
4.5%
E 33716
 
3.4%
S 30480
 
3.1%
R 25125
 
2.5%
G 24740
 
2.5%
Other values (28) 285766
28.8%

level1Name
Text

Missing 

Distinct1634
Distinct (%)1.2%
Missing855020
Missing (%)86.5%
Memory size7.5 MiB
2025-01-07T10:43:45.460066image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length32
Median length29
Mean length10.18645694
Min length3

Characters and Unicode

Total characters1358690
Distinct characters120
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique323 ?
Unique (%)0.2%

Sample

1st rowSan Juan
2nd rowBío-Bío
3rd rowCuyuni-Mazaruni
4th rowVirginia
5th rowAcre
ValueCountFrequency (%)
amazonas 5737
 
3.2%
upper 4920
 
2.7%
maryland 4566
 
2.5%
essequibo 4171
 
2.3%
potaro-siparuni 4001
 
2.2%
virginia 3991
 
2.2%
takutu-upper 3952
 
2.2%
columbia 3813
 
2.1%
cuyuni-mazaruni 3604
 
2.0%
district 3288
 
1.8%
Other values (1775) 138660
76.7%
2025-01-07T10:43:45.726097image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 199066
14.7%
i 109009
 
8.0%
r 94704
 
7.0%
n 89929
 
6.6%
o 85891
 
6.3%
e 69436
 
5.1%
u 67791
 
5.0%
s 50157
 
3.7%
t 48416
 
3.6%
47321
 
3.5%
Other values (110) 496970
36.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1358690
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 199066
14.7%
i 109009
 
8.0%
r 94704
 
7.0%
n 89929
 
6.6%
o 85891
 
6.3%
e 69436
 
5.1%
u 67791
 
5.0%
s 50157
 
3.7%
t 48416
 
3.6%
47321
 
3.5%
Other values (110) 496970
36.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1358690
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 199066
14.7%
i 109009
 
8.0%
r 94704
 
7.0%
n 89929
 
6.6%
o 85891
 
6.3%
e 69436
 
5.1%
u 67791
 
5.0%
s 50157
 
3.7%
t 48416
 
3.6%
47321
 
3.5%
Other values (110) 496970
36.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1358690
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 199066
14.7%
i 109009
 
8.0%
r 94704
 
7.0%
n 89929
 
6.6%
o 85891
 
6.3%
e 69436
 
5.1%
u 67791
 
5.0%
s 50157
 
3.7%
t 48416
 
3.6%
47321
 
3.5%
Other values (110) 496970
36.6%

level2Gid
Text

Missing 

Distinct7917
Distinct (%)6.1%
Missing859029
Missing (%)86.9%
Memory size7.5 MiB
2025-01-07T10:43:45.915678image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length12
Median length11
Mean length9.905212061
Min length8

Characters and Unicode

Total characters1281467
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2525 ?
Unique (%)2.0%

Sample

1st rowDOM.26.2_1
2nd rowCHL.6.3_1
3rd rowGUY.2.5_1
4th rowUSA.47.8_1
5th rowBRA.1.11_2
ValueCountFrequency (%)
usa.9.1_1 3286
 
2.5%
guy.8.8_1 3032
 
2.3%
guy.2.8_1 2312
 
1.8%
guy.10.4_1 2189
 
1.7%
usa.21.15_1 1956
 
1.5%
usa.21.16_1 1386
 
1.1%
ven.6.5_1 1255
 
1.0%
ven.1.7_1 1231
 
1.0%
usa.47.102_1 1092
 
0.8%
usa.2.17_1 1081
 
0.8%
Other values (7907) 110553
85.5%
2025-01-07T10:43:46.168899image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 258687
20.2%
1 192666
15.0%
_ 129373
 
10.1%
2 95635
 
7.5%
U 55286
 
4.3%
A 48244
 
3.8%
4 37638
 
2.9%
E 33642
 
2.6%
3 32693
 
2.6%
S 29992
 
2.3%
Other values (28) 367611
28.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1281467
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 258687
20.2%
1 192666
15.0%
_ 129373
 
10.1%
2 95635
 
7.5%
U 55286
 
4.3%
A 48244
 
3.8%
4 37638
 
2.9%
E 33642
 
2.6%
3 32693
 
2.6%
S 29992
 
2.3%
Other values (28) 367611
28.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1281467
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 258687
20.2%
1 192666
15.0%
_ 129373
 
10.1%
2 95635
 
7.5%
U 55286
 
4.3%
A 48244
 
3.8%
4 37638
 
2.9%
E 33642
 
2.6%
3 32693
 
2.6%
S 29992
 
2.3%
Other values (28) 367611
28.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1281467
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 258687
20.2%
1 192666
15.0%
_ 129373
 
10.1%
2 95635
 
7.5%
U 55286
 
4.3%
A 48244
 
3.8%
4 37638
 
2.9%
E 33642
 
2.6%
3 32693
 
2.6%
S 29992
 
2.3%
Other values (28) 367611
28.7%

level2Name
Text

Missing 

Distinct7281
Distinct (%)5.6%
Missing859040
Missing (%)86.9%
Memory size7.5 MiB
2025-01-07T10:43:46.369575image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length32
Median length27
Mean length10.8988652
Min length1

Characters and Unicode

Total characters1409899
Distinct characters144
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2224 ?
Unique (%)1.7%

Sample

1st rowEl Cercado
2nd rowConcepción
3rd rowKamarang
4th rowArlington
5th rowManoel Urbano
ValueCountFrequency (%)
of 11716
 
5.2%
rest 8168
 
3.6%
region 8145
 
3.6%
3557
 
1.6%
de 3551
 
1.6%
district 3288
 
1.5%
columbia 3288
 
1.5%
8 3040
 
1.4%
san 2745
 
1.2%
prince 2492
 
1.1%
Other values (7516) 174499
77.7%
2025-01-07T10:43:46.635265image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 163519
 
11.6%
o 112697
 
8.0%
e 96344
 
6.8%
95127
 
6.7%
i 93298
 
6.6%
n 91747
 
6.5%
r 79497
 
5.6%
t 58541
 
4.2%
u 49822
 
3.5%
l 48733
 
3.5%
Other values (134) 520574
36.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1409899
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 163519
 
11.6%
o 112697
 
8.0%
e 96344
 
6.8%
95127
 
6.7%
i 93298
 
6.6%
n 91747
 
6.5%
r 79497
 
5.6%
t 58541
 
4.2%
u 49822
 
3.5%
l 48733
 
3.5%
Other values (134) 520574
36.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1409899
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 163519
 
11.6%
o 112697
 
8.0%
e 96344
 
6.8%
95127
 
6.7%
i 93298
 
6.6%
n 91747
 
6.5%
r 79497
 
5.6%
t 58541
 
4.2%
u 49822
 
3.5%
l 48733
 
3.5%
Other values (134) 520574
36.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1409899
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 163519
 
11.6%
o 112697
 
8.0%
e 96344
 
6.8%
95127
 
6.7%
i 93298
 
6.6%
n 91747
 
6.5%
r 79497
 
5.6%
t 58541
 
4.2%
u 49822
 
3.5%
l 48733
 
3.5%
Other values (134) 520574
36.9%

level3Gid
Text

Missing 

Distinct4058
Distinct (%)11.6%
Missing953538
Missing (%)96.5%
Memory size7.5 MiB
2025-01-07T10:43:46.851148image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length36
Median length15
Mean length11.74667279
Min length11

Characters and Unicode

Total characters409536
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1545 ?
Unique (%)4.4%

Sample

1st rowCHL.6.3.12_1
2nd rowPER.18.1.3_1
3rd rowCRI.4.5.4_1
4th rowECU.21.2.1_1
5th rowPER.8.9.1_1
ValueCountFrequency (%)
per.8.9.1_1 481
 
1.4%
per.18.3.4_1 344
 
1.0%
ecu.14.14.2_1 335
 
1.0%
bol.4.17.4_2 316
 
0.9%
can.6.1.8_1 291
 
0.8%
ecu.17.4.1_1 285
 
0.8%
bol.8.14.1_2 276
 
0.8%
can.13.1.35_1 214
 
0.6%
bol.4.18.2_2 207
 
0.6%
per.20.2.4_1 189
 
0.5%
Other values (4048) 31926
91.6%
2025-01-07T10:43:47.124344image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 104589
25.5%
1 65183
15.9%
_ 34863
 
8.5%
2 26503
 
6.5%
C 15795
 
3.9%
4 15291
 
3.7%
3 14173
 
3.5%
E 12203
 
3.0%
6 9734
 
2.4%
5 9308
 
2.3%
Other values (32) 101894
24.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 409536
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 104589
25.5%
1 65183
15.9%
_ 34863
 
8.5%
2 26503
 
6.5%
C 15795
 
3.9%
4 15291
 
3.7%
3 14173
 
3.5%
E 12203
 
3.0%
6 9734
 
2.4%
5 9308
 
2.3%
Other values (32) 101894
24.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 409536
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 104589
25.5%
1 65183
15.9%
_ 34863
 
8.5%
2 26503
 
6.5%
C 15795
 
3.9%
4 15291
 
3.7%
3 14173
 
3.5%
E 12203
 
3.0%
6 9734
 
2.4%
5 9308
 
2.3%
Other values (32) 101894
24.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 409536
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 104589
25.5%
1 65183
15.9%
_ 34863
 
8.5%
2 26503
 
6.5%
C 15795
 
3.9%
4 15291
 
3.7%
3 14173
 
3.5%
E 12203
 
3.0%
6 9734
 
2.4%
5 9308
 
2.3%
Other values (32) 101894
24.9%

level3Name
Text

Missing 

Distinct3831
Distinct (%)11.1%
Missing953860
Missing (%)96.5%
Memory size7.5 MiB
2025-01-07T10:43:47.333658image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length32
Median length28
Mean length10.60914249
Min length2

Characters and Unicode

Total characters366461
Distinct characters127
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1425 ?
Unique (%)4.1%

Sample

1st rowTomé
2nd rowManu
3rd rowSan José
4th rowAlluriquin
5th rowEcharate
ValueCountFrequency (%)
san 1730
 
3.1%
de 1393
 
2.5%
unorganized 1082
 
1.9%
la 844
 
1.5%
el 708
 
1.3%
no 616
 
1.1%
division 487
 
0.9%
echarate 481
 
0.9%
santa 470
 
0.8%
en 449
 
0.8%
Other values (4214) 48076
85.3%
2025-01-07T10:43:47.702433image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 52267
 
14.3%
n 25746
 
7.0%
o 25629
 
7.0%
i 23308
 
6.4%
21794
 
5.9%
e 21436
 
5.8%
r 18083
 
4.9%
u 14350
 
3.9%
l 13877
 
3.8%
t 11765
 
3.2%
Other values (117) 138206
37.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 366461
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 52267
 
14.3%
n 25746
 
7.0%
o 25629
 
7.0%
i 23308
 
6.4%
21794
 
5.9%
e 21436
 
5.8%
r 18083
 
4.9%
u 14350
 
3.9%
l 13877
 
3.8%
t 11765
 
3.2%
Other values (117) 138206
37.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 366461
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 52267
 
14.3%
n 25746
 
7.0%
o 25629
 
7.0%
i 23308
 
6.4%
21794
 
5.9%
e 21436
 
5.8%
r 18083
 
4.9%
u 14350
 
3.9%
l 13877
 
3.8%
t 11765
 
3.2%
Other values (117) 138206
37.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 366461
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 52267
 
14.3%
n 25746
 
7.0%
o 25629
 
7.0%
i 23308
 
6.4%
21794
 
5.9%
e 21436
 
5.8%
r 18083
 
4.9%
u 14350
 
3.9%
l 13877
 
3.8%
t 11765
 
3.2%
Other values (117) 138206
37.7%

iucnRedListCategory
Text

Missing 

Distinct10
Distinct (%)< 0.1%
Missing91545
Missing (%)9.3%
Memory size7.5 MiB
2025-01-07T10:43:47.760939image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length2
Mean length2.00002453
Min length2

Characters and Unicode

Total characters1793736
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNE
2nd rowNE
3rd rowNE
4th rowNE
5th rowNE
ValueCountFrequency (%)
ne 712718
79.5%
lc 165443
 
18.4%
vu 6108
 
0.7%
en 4438
 
0.5%
nt 3884
 
0.4%
dd 2382
 
0.3%
cr 1766
 
0.2%
ew 91
 
< 0.1%
ex 26
 
< 0.1%
2024-12-02t13:56:28.527z 1
 
< 0.1%
2025-01-07T10:43:47.854156image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 721040
40.2%
E 717273
40.0%
C 167209
 
9.3%
L 165443
 
9.2%
V 6108
 
0.3%
U 6108
 
0.3%
D 4764
 
0.3%
T 3885
 
0.2%
R 1766
 
0.1%
W 91
 
< 0.1%
Other values (14) 49
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1793736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 721040
40.2%
E 717273
40.0%
C 167209
 
9.3%
L 165443
 
9.2%
V 6108
 
0.3%
U 6108
 
0.3%
D 4764
 
0.3%
T 3885
 
0.2%
R 1766
 
0.1%
W 91
 
< 0.1%
Other values (14) 49
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1793736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 721040
40.2%
E 717273
40.0%
C 167209
 
9.3%
L 165443
 
9.2%
V 6108
 
0.3%
U 6108
 
0.3%
D 4764
 
0.3%
T 3885
 
0.2%
R 1766
 
0.1%
W 91
 
< 0.1%
Other values (14) 49
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1793736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 721040
40.2%
E 717273
40.0%
C 167209
 
9.3%
L 165443
 
9.2%
V 6108
 
0.3%
U 6108
 
0.3%
D 4764
 
0.3%
T 3885
 
0.2%
R 1766
 
0.1%
W 91
 
< 0.1%
Other values (14) 49
 
< 0.1%

Unnamed: 223
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean500
Minimum500
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:47.907371image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile500
Q1500
median500
Q3500
95-th percentile500
Maximum500
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Mean500
Median Absolute Deviation (MAD)0
Skewnessnan
Sum500
Variancenan
MonotonicityStrictly increasing
2025-01-07T10:43:47.952374image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
500 1
 
< 0.1%
(Missing) 988401
> 99.9%
ValueCountFrequency (%)
500 1
< 0.1%
ValueCountFrequency (%)
500 1
< 0.1%

Unnamed: 224
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean0
Minimum0
Maximum0
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:47.993674image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Mean0
Median Absolute Deviation (MAD)0
Skewnessnan
Sum0
Variancenan
MonotonicityStrictly increasing
2025-01-07T10:43:48.035182image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
0 1
 
< 0.1%
(Missing) 988401
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
ValueCountFrequency (%)
0 1
< 0.1%

Unnamed: 225
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

Unnamed: 226
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

Unnamed: 227
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

Unnamed: 228
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Memory size7.5 MiB
2025-01-07T10:43:48.078823image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length48
Median length48
Mean length48
Min length48

Characters and Unicode

Total characters48
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowOCCURRENCE_STATUS_INFERRED_FROM_INDIVIDUAL_COUNT
ValueCountFrequency (%)
occurrence_status_inferred_from_individual_count 1
100.0%
2025-01-07T10:43:48.184906image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 5
10.4%
_ 5
10.4%
U 4
8.3%
C 4
8.3%
N 4
8.3%
E 4
8.3%
I 4
8.3%
O 3
 
6.2%
D 3
 
6.2%
T 3
 
6.2%
Other values (6) 9
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 48
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 5
10.4%
_ 5
10.4%
U 4
8.3%
C 4
8.3%
N 4
8.3%
E 4
8.3%
I 4
8.3%
O 3
 
6.2%
D 3
 
6.2%
T 3
 
6.2%
Other values (6) 9
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 48
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 5
10.4%
_ 5
10.4%
U 4
8.3%
C 4
8.3%
N 4
8.3%
E 4
8.3%
I 4
8.3%
O 3
 
6.2%
D 3
 
6.2%
T 3
 
6.2%
Other values (6) 9
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 48
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 5
10.4%
_ 5
10.4%
U 4
8.3%
C 4
8.3%
N 4
8.3%
E 4
8.3%
I 4
8.3%
O 3
 
6.2%
D 3
 
6.2%
T 3
 
6.2%
Other values (6) 9
18.8%

Unnamed: 229
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Memory size7.5 MiB
2025-01-07T10:43:48.232040image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters10
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowStillImage
ValueCountFrequency (%)
stillimage 1
100.0%
2025-01-07T10:43:48.325095image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 2
20.0%
S 1
10.0%
t 1
10.0%
i 1
10.0%
I 1
10.0%
m 1
10.0%
a 1
10.0%
g 1
10.0%
e 1
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 2
20.0%
S 1
10.0%
t 1
10.0%
i 1
10.0%
I 1
10.0%
m 1
10.0%
a 1
10.0%
g 1
10.0%
e 1
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 2
20.0%
S 1
10.0%
t 1
10.0%
i 1
10.0%
I 1
10.0%
m 1
10.0%
a 1
10.0%
g 1
10.0%
e 1
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 2
20.0%
S 1
10.0%
t 1
10.0%
i 1
10.0%
I 1
10.0%
m 1
10.0%
a 1
10.0%
g 1
10.0%
e 1
10.0%

Unnamed: 230
Boolean

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Memory size7.5 MiB
False
 
1
(Missing)
988401 
ValueCountFrequency (%)
False 1
 
< 0.1%
(Missing) 988401
> 99.9%
2025-01-07T10:43:48.375095image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Unnamed: 231
Boolean

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Memory size7.5 MiB
False
 
1
(Missing)
988401 
ValueCountFrequency (%)
False 1
 
< 0.1%
(Missing) 988401
> 99.9%
2025-01-07T10:43:48.408030image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Unnamed: 232
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean7634583
Minimum7634583
Maximum7634583
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:48.446069image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum7634583
5-th percentile7634583
Q17634583
median7634583
Q37634583
95-th percentile7634583
Maximum7634583
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Mean7634583
Median Absolute Deviation (MAD)0
Skewnessnan
Sum7634583
Variancenan
MonotonicityStrictly increasing
2025-01-07T10:43:48.494483image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
7634583 1
 
< 0.1%
(Missing) 988401
> 99.9%
ValueCountFrequency (%)
7634583 1
< 0.1%
ValueCountFrequency (%)
7634583 1
< 0.1%

Unnamed: 233
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean5541644
Minimum5541644
Maximum5541644
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:48.541492image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum5541644
5-th percentile5541644
Q15541644
median5541644
Q35541644
95-th percentile5541644
Maximum5541644
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Mean5541644
Median Absolute Deviation (MAD)0
Skewnessnan
Sum5541644
Variancenan
MonotonicityStrictly increasing
2025-01-07T10:43:48.588814image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
5541644 1
 
< 0.1%
(Missing) 988401
> 99.9%
ValueCountFrequency (%)
5541644 1
< 0.1%
ValueCountFrequency (%)
5541644 1
< 0.1%

Unnamed: 234
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean6
Minimum6
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:48.632267image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile6
Q16
median6
Q36
95-th percentile6
Maximum6
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Mean6
Median Absolute Deviation (MAD)0
Skewnessnan
Sum6
Variancenan
MonotonicityStrictly increasing
2025-01-07T10:43:48.675776image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
6 1
 
< 0.1%
(Missing) 988401
> 99.9%
ValueCountFrequency (%)
6 1
< 0.1%
ValueCountFrequency (%)
6 1
< 0.1%

Unnamed: 235
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean7707728
Minimum7707728
Maximum7707728
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:48.719814image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum7707728
5-th percentile7707728
Q17707728
median7707728
Q37707728
95-th percentile7707728
Maximum7707728
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Mean7707728
Median Absolute Deviation (MAD)0
Skewnessnan
Sum7707728
Variancenan
MonotonicityStrictly increasing
2025-01-07T10:43:48.764812image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
7707728 1
 
< 0.1%
(Missing) 988401
> 99.9%
ValueCountFrequency (%)
7707728 1
< 0.1%
ValueCountFrequency (%)
7707728 1
< 0.1%

Unnamed: 236
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean220
Minimum220
Maximum220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:48.807650image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum220
5-th percentile220
Q1220
median220
Q3220
95-th percentile220
Maximum220
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Mean220
Median Absolute Deviation (MAD)0
Skewnessnan
Sum220
Variancenan
MonotonicityStrictly increasing
2025-01-07T10:43:48.850653image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
220 1
 
< 0.1%
(Missing) 988401
> 99.9%
ValueCountFrequency (%)
220 1
< 0.1%
ValueCountFrequency (%)
220 1
< 0.1%

Unnamed: 237
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean718
Minimum718
Maximum718
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:48.892935image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum718
5-th percentile718
Q1718
median718
Q3718
95-th percentile718
Maximum718
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Mean718
Median Absolute Deviation (MAD)0
Skewnessnan
Sum718
Variancenan
MonotonicityStrictly increasing
2025-01-07T10:43:48.938446image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
718 1
 
< 0.1%
(Missing) 988401
> 99.9%
ValueCountFrequency (%)
718 1
< 0.1%
ValueCountFrequency (%)
718 1
< 0.1%

Unnamed: 238
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean9291
Minimum9291
Maximum9291
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:48.981849image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum9291
5-th percentile9291
Q19291
median9291
Q39291
95-th percentile9291
Maximum9291
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Mean9291
Median Absolute Deviation (MAD)0
Skewnessnan
Sum9291
Variancenan
MonotonicityStrictly increasing
2025-01-07T10:43:49.026373image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
9291 1
 
< 0.1%
(Missing) 988401
> 99.9%
ValueCountFrequency (%)
9291 1
< 0.1%
ValueCountFrequency (%)
9291 1
< 0.1%

Unnamed: 239
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean3155252
Minimum3155252
Maximum3155252
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:49.071374image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum3155252
5-th percentile3155252
Q13155252
median3155252
Q33155252
95-th percentile3155252
Maximum3155252
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Mean3155252
Median Absolute Deviation (MAD)0
Skewnessnan
Sum3155252
Variancenan
MonotonicityStrictly increasing
2025-01-07T10:43:49.116931image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
3155252 1
 
< 0.1%
(Missing) 988401
> 99.9%
ValueCountFrequency (%)
3155252 1
< 0.1%
ValueCountFrequency (%)
3155252 1
< 0.1%

Unnamed: 240
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

Unnamed: 241
Real number (ℝ)

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Infinite0
Infinite (%)0.0%
Mean5541644
Minimum5541644
Maximum5541644
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 MiB
2025-01-07T10:43:49.161933image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum5541644
5-th percentile5541644
Q15541644
median5541644
Q35541644
95-th percentile5541644
Maximum5541644
Range0
Interquartile range (IQR)0

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Mean5541644
Median Absolute Deviation (MAD)0
Skewnessnan
Sum5541644
Variancenan
MonotonicityStrictly increasing
2025-01-07T10:43:49.207947image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=1)
ValueCountFrequency (%)
5541644 1
 
< 0.1%
(Missing) 988401
> 99.9%
ValueCountFrequency (%)
5541644 1
< 0.1%
ValueCountFrequency (%)
5541644 1
< 0.1%

Unnamed: 242
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Memory size7.5 MiB
2025-01-07T10:43:49.238950image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters13
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowAnnona edulis
ValueCountFrequency (%)
annona 1
50.0%
edulis 1
50.0%
2025-01-07T10:43:49.336246image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 3
23.1%
A 1
 
7.7%
o 1
 
7.7%
a 1
 
7.7%
1
 
7.7%
e 1
 
7.7%
d 1
 
7.7%
u 1
 
7.7%
l 1
 
7.7%
i 1
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 3
23.1%
A 1
 
7.7%
o 1
 
7.7%
a 1
 
7.7%
1
 
7.7%
e 1
 
7.7%
d 1
 
7.7%
u 1
 
7.7%
l 1
 
7.7%
i 1
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 3
23.1%
A 1
 
7.7%
o 1
 
7.7%
a 1
 
7.7%
1
 
7.7%
e 1
 
7.7%
d 1
 
7.7%
u 1
 
7.7%
l 1
 
7.7%
i 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 3
23.1%
A 1
 
7.7%
o 1
 
7.7%
a 1
 
7.7%
1
 
7.7%
e 1
 
7.7%
d 1
 
7.7%
u 1
 
7.7%
l 1
 
7.7%
i 1
 
7.7%

Unnamed: 243
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Memory size7.5 MiB
2025-01-07T10:43:49.386722image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length41
Median length41
Mean length41
Min length41

Characters and Unicode

Total characters41
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowAnnona edulis (Triana & Planch.) H.Rainer
ValueCountFrequency (%)
annona 1
16.7%
edulis 1
16.7%
triana 1
16.7%
1
16.7%
planch 1
16.7%
h.rainer 1
16.7%
2025-01-07T10:43:49.488602image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 6
14.6%
a 5
12.2%
5
12.2%
i 3
 
7.3%
l 2
 
4.9%
e 2
 
4.9%
r 2
 
4.9%
. 2
 
4.9%
o 1
 
2.4%
A 1
 
2.4%
Other values (12) 12
29.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 6
14.6%
a 5
12.2%
5
12.2%
i 3
 
7.3%
l 2
 
4.9%
e 2
 
4.9%
r 2
 
4.9%
. 2
 
4.9%
o 1
 
2.4%
A 1
 
2.4%
Other values (12) 12
29.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 6
14.6%
a 5
12.2%
5
12.2%
i 3
 
7.3%
l 2
 
4.9%
e 2
 
4.9%
r 2
 
4.9%
. 2
 
4.9%
o 1
 
2.4%
A 1
 
2.4%
Other values (12) 12
29.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 6
14.6%
a 5
12.2%
5
12.2%
i 3
 
7.3%
l 2
 
4.9%
e 2
 
4.9%
r 2
 
4.9%
. 2
 
4.9%
o 1
 
2.4%
A 1
 
2.4%
Other values (12) 12
29.3%

Unnamed: 244
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Memory size7.5 MiB
2025-01-07T10:43:49.534108image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length26
Median length26
Mean length26
Min length26

Characters and Unicode

Total characters26
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowRollinia edulis var. acuta
ValueCountFrequency (%)
rollinia 1
25.0%
edulis 1
25.0%
var 1
25.0%
acuta 1
25.0%
2025-01-07T10:43:49.632599image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 4
15.4%
i 3
11.5%
3
11.5%
l 3
11.5%
u 2
 
7.7%
R 1
 
3.8%
n 1
 
3.8%
o 1
 
3.8%
e 1
 
3.8%
d 1
 
3.8%
Other values (6) 6
23.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 4
15.4%
i 3
11.5%
3
11.5%
l 3
11.5%
u 2
 
7.7%
R 1
 
3.8%
n 1
 
3.8%
o 1
 
3.8%
e 1
 
3.8%
d 1
 
3.8%
Other values (6) 6
23.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 4
15.4%
i 3
11.5%
3
11.5%
l 3
11.5%
u 2
 
7.7%
R 1
 
3.8%
n 1
 
3.8%
o 1
 
3.8%
e 1
 
3.8%
d 1
 
3.8%
Other values (6) 6
23.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 4
15.4%
i 3
11.5%
3
11.5%
l 3
11.5%
u 2
 
7.7%
R 1
 
3.8%
n 1
 
3.8%
o 1
 
3.8%
e 1
 
3.8%
d 1
 
3.8%
Other values (6) 6
23.1%

Unnamed: 245
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

Unnamed: 246
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Memory size7.5 MiB
2025-01-07T10:43:49.673598image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowEML
ValueCountFrequency (%)
eml 1
100.0%
2025-01-07T10:43:49.764400image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 1
33.3%
M 1
33.3%
L 1
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 1
33.3%
M 1
33.3%
L 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 1
33.3%
M 1
33.3%
L 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 1
33.3%
M 1
33.3%
L 1
33.3%

Unnamed: 247
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Memory size7.5 MiB
2025-01-07T10:43:49.813182image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters24
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row2024-12-02T13:56:28.527Z
ValueCountFrequency (%)
2024-12-02t13:56:28.527z 1
100.0%
2025-01-07T10:43:49.911701image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 6
25.0%
0 2
 
8.3%
- 2
 
8.3%
1 2
 
8.3%
5 2
 
8.3%
: 2
 
8.3%
T 1
 
4.2%
4 1
 
4.2%
3 1
 
4.2%
6 1
 
4.2%
Other values (4) 4
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 6
25.0%
0 2
 
8.3%
- 2
 
8.3%
1 2
 
8.3%
5 2
 
8.3%
: 2
 
8.3%
T 1
 
4.2%
4 1
 
4.2%
3 1
 
4.2%
6 1
 
4.2%
Other values (4) 4
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 6
25.0%
0 2
 
8.3%
- 2
 
8.3%
1 2
 
8.3%
5 2
 
8.3%
: 2
 
8.3%
T 1
 
4.2%
4 1
 
4.2%
3 1
 
4.2%
6 1
 
4.2%
Other values (4) 4
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 6
25.0%
0 2
 
8.3%
- 2
 
8.3%
1 2
 
8.3%
5 2
 
8.3%
: 2
 
8.3%
T 1
 
4.2%
4 1
 
4.2%
3 1
 
4.2%
6 1
 
4.2%
Other values (4) 4
16.7%

Unnamed: 248
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Memory size7.5 MiB
2025-01-07T10:43:49.962701image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters24
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row2024-12-02T11:48:23.416Z
ValueCountFrequency (%)
2024-12-02t11:48:23.416z 1
100.0%
2025-01-07T10:43:50.060396image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 5
20.8%
1 4
16.7%
4 3
12.5%
0 2
 
8.3%
- 2
 
8.3%
: 2
 
8.3%
T 1
 
4.2%
8 1
 
4.2%
3 1
 
4.2%
. 1
 
4.2%
Other values (2) 2
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 5
20.8%
1 4
16.7%
4 3
12.5%
0 2
 
8.3%
- 2
 
8.3%
: 2
 
8.3%
T 1
 
4.2%
8 1
 
4.2%
3 1
 
4.2%
. 1
 
4.2%
Other values (2) 2
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 5
20.8%
1 4
16.7%
4 3
12.5%
0 2
 
8.3%
- 2
 
8.3%
: 2
 
8.3%
T 1
 
4.2%
8 1
 
4.2%
3 1
 
4.2%
. 1
 
4.2%
Other values (2) 2
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 5
20.8%
1 4
16.7%
4 3
12.5%
0 2
 
8.3%
- 2
 
8.3%
: 2
 
8.3%
T 1
 
4.2%
8 1
 
4.2%
3 1
 
4.2%
. 1
 
4.2%
Other values (2) 2
 
8.3%

Unnamed: 249
Boolean

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Memory size7.5 MiB
True
 
1
(Missing)
988401 
ValueCountFrequency (%)
True 1
 
< 0.1%
(Missing) 988401
> 99.9%
2025-01-07T10:43:50.110578image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Unnamed: 250
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

Unnamed: 251
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

Unnamed: 252
Boolean

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Memory size7.5 MiB
False
 
1
(Missing)
988401 
ValueCountFrequency (%)
False 1
 
< 0.1%
(Missing) 988401
> 99.9%
2025-01-07T10:43:50.142578image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Unnamed: 253
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Memory size7.5 MiB
2025-01-07T10:43:50.171581image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters13
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowLATIN_AMERICA
ValueCountFrequency (%)
latin_america 1
100.0%
2025-01-07T10:43:50.264505image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 3
23.1%
I 2
15.4%
L 1
 
7.7%
T 1
 
7.7%
N 1
 
7.7%
_ 1
 
7.7%
M 1
 
7.7%
E 1
 
7.7%
R 1
 
7.7%
C 1
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 3
23.1%
I 2
15.4%
L 1
 
7.7%
T 1
 
7.7%
N 1
 
7.7%
_ 1
 
7.7%
M 1
 
7.7%
E 1
 
7.7%
R 1
 
7.7%
C 1
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 3
23.1%
I 2
15.4%
L 1
 
7.7%
T 1
 
7.7%
N 1
 
7.7%
_ 1
 
7.7%
M 1
 
7.7%
E 1
 
7.7%
R 1
 
7.7%
C 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 3
23.1%
I 2
15.4%
L 1
 
7.7%
T 1
 
7.7%
N 1
 
7.7%
_ 1
 
7.7%
M 1
 
7.7%
E 1
 
7.7%
R 1
 
7.7%
C 1
 
7.7%

Unnamed: 254
Text

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing988401
Missing (%)> 99.9%
Memory size7.5 MiB
2025-01-07T10:43:50.309947image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters13
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowNORTH_AMERICA
ValueCountFrequency (%)
north_america 1
100.0%
2025-01-07T10:43:50.406128image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 2
15.4%
A 2
15.4%
N 1
7.7%
O 1
7.7%
T 1
7.7%
H 1
7.7%
_ 1
7.7%
M 1
7.7%
E 1
7.7%
I 1
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 2
15.4%
A 2
15.4%
N 1
7.7%
O 1
7.7%
T 1
7.7%
H 1
7.7%
_ 1
7.7%
M 1
7.7%
E 1
7.7%
I 1
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 2
15.4%
A 2
15.4%
N 1
7.7%
O 1
7.7%
T 1
7.7%
H 1
7.7%
_ 1
7.7%
M 1
7.7%
E 1
7.7%
I 1
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 2
15.4%
A 2
15.4%
N 1
7.7%
O 1
7.7%
T 1
7.7%
H 1
7.7%
_ 1
7.7%
M 1
7.7%
E 1
7.7%
I 1
7.7%

Unnamed: 255
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

Unnamed: 256
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

Unnamed: 257
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

Unnamed: 258
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

Unnamed: 259
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

Unnamed: 260
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

Unnamed: 261
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

Unnamed: 262
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB

Unnamed: 263
Unsupported

Missing  Rejected  Unsupported 

Missing988402
Missing (%)100.0%
Memory size7.5 MiB